1. bookVolume 6 (2021): Issue 1 (February 2021)
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eISSN
2543-683X
First Published
30 Mar 2017
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4 times per year
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English
access type Open Access

A Scientometric Approach to Analyze Scientific Development on Renewable Energy Sources

Published Online: 27 Nov 2020
Volume & Issue: Volume 6 (2021) - Issue 1 (February 2021)
Page range: 87 - 119
Received: 22 Apr 2020
Accepted: 10 Sep 2020
Journal Details
License
Format
Journal
eISSN
2543-683X
First Published
30 Mar 2017
Publication timeframe
4 times per year
Languages
English
AbstractPurpose

This paper aims to point out the scientific development and research density of renewable energy sources such as photovoltaic, wind, and biomass, using a mix of computational tools. Based on this, it was possible to verify the existence of new research trends and opportunities in a macro view regarding management, performance evaluation, and decision-making in renewable energy generation systems and installations.

Design/methodology/approach

A scientometric approach was used based on a research protocol to retrieve papers from the Scopus database, and through four scientometric questions, to analyze each area. Software such as the Science Mapping Analysis Software Tool (SciMAT) and Sci2 Tool were used to map the science development and density.

Findings

The scientific development of renewable energy areas is highlighted, pointing out research opportunities regarding management, studies on costs and investments, systemic diagnosis, and performance evaluation for decision-making in businesses in these areas.

Research limitations

This paper was limited to the articles indexed in the Scopus database and by the questions used to analyze the scientific development of renewable energy areas.

Practical implications

The results show the need for a managerial perspective in businesses related to renewable energy sources at the managerial, technical, and operational levels, including performance evaluation, assertive decision making, and adequate use of technical and financial resources.

Originality/value

This paper shows that there is a research field to be explored, with gaps to fill and further research to be carried out in this area. Besides, this paper can serve as a basis for other studies and research in other areas and domains.

Keywords

Introduction

Sustainable energy generation holds great promise for resolving current energy and environmental challenges (Hu et al., 2017) together with strategies for managing limited energy resources (Schaefer et al., 2020). The main goal of increasing the spread and use of renewable energy generation systems is to reduce the use of fossil fuels and greenhouse gas emissions (Roselli et al., 2019). The transition to sustainable energy can be achieved in many ways (Longo, Foiadelli, & Yaïci, 2019) and renewable energy structures must be managed with care so that they do not harm the environment (Ben Jebli, Ben Youssef, & Apergis, 2019). Growing concern about the effects of energy production and the use of fossil fuels on global warming also generates investment opportunities in energy efficiency and renewable energy projects (Martí-Ballester, 2019). Photovoltaic (PV) and wind energy sources are the most widespread since their maturity allows generation with a high degree of efficiency (Lopez-Rey et al., 2019), but biomass can also be considered as an efficient renewable energy generation system (Ducom et al., 2020).

These three sources of renewable energy generation account for just over 60% of the total jobs generated in renewable energy generation plants (IRENA, 2018). Solar energy has been the subject of extensive studies and received numerous applications around the world for the production of electricity in residential and commercial sectors (Chel & Kaushik, 2018). It has a characteristic that favors its massive penetration in the urban environment because solar radiation is received everywhere with levels of intensity that allow the production of electricity (Lopez-Rey et al., 2019), being determinant for this market advance (Rosa et al., 2018). Wind energy is clean, abundant, inexhaustible, and environmentally preferable (Oyedepo, Adaramola, & Paul, 2012) and refers to the development of wind farms near beaches, rivers, or lakes to produce electricity from the wind (Tarfaoui, Nachtane, & Boudounit, 2019). Finally, biomass energy is clean and renewable energy and is also considered an alternative to fossil fuels (Liu, Feng, & He, 2019), as it can contribute significantly to reduce the environmental impacts associated with using waste from other processes for generation, such as the use of residues from olive oil production (Ducom et al., 2020).

As introduced above, this article focuses on these three types of sustainable energy generation, seeking to show the basic concepts and to show if there is something in common between these different areas of research and the advances obtained, identifying the potential industry–university–research collaboration (Xu et al., 2020). In this sense, due to the increase in energy demand, researchers are looking for reliable and efficient techniques and applications related to management, through which the most appropriate type of energy source is chosen, considering different criteria (Chen et al., 2017). Therefore, it is interesting to assess in which areas further progress can be made, where studies are still few, and how this can assist in the development and management of these technologies. This is important because the dissemination, knowledge, and control of these renewable energy sources will lead to a better level of growth, reducing environmental risks and consequently improving the social, financial and economic well-being of the world (Haseeb et al., 2019). Taking these aspects into a broad approach, the purpose of this paper is to determine the scientific development and research density in the areas that make up the main renewable energies: wind energy, photovoltaic energy and biomass energy, and, from this, to verify the existence of new research trends and opportunities in a macro view, regarding management, performance evaluation and decision-making in renewable energy generation systems and installations.

To achieve this purpose, a scientometric analysis of publications on renewable energy sources was done. Scientometrics is the science of measuring and analyzing quantitatively the advancement of science (Abramo, 2018; Sassmannshausen & Volkmann, 2018), and is used to evaluate the performance of research and science areas, and to identify research gaps and trends (Kumar & Kaliyaperumal, 2015). This analysis uses methods for science mapping to transform data, networks, and maps into useful knowledge (Cobo et al., 2011b). These science mapping methods can involve temporal or longitudinal analysis (de Solla Price & Gürsey, 1975; Garfield, 1994), network analysis (Carrington, Scott, & Wasserman, 2005; Cook & Holder, 2006; Newman, 2001a, 2001b; Skillicorn, 2007; Wasserman & Faust, 1994; Wu, 2019), performance analysis (Cobo et al., 2011b), or geospatial analysis (Batty, 2003; Leydesdorff & Persson, 2010; Small & Garfield, 1985; Wuestman, Hoekman, & Frenken, 2019). Light, Polley, and Börner (2014) proposed organizing these methods into four questions:

“When?”: these are temporal studies that make analyses in chronological order;

“Who?”: this type of study analyzes the information obtained through a map of networks elaborated with algorithms and techniques of information science;

“What?”: language analysis elucidates this question to answer what is being researched;

“Where?”: geospatial studies based on cartographic analyses.

These four questions were used to analyze the research on renewable energy sources, providing convincing analysis of the extent of the studies (Garner et al., 2020) and guiding new non-repetitive studies. To accomplish this approach, computational tools such as Sci2 Tool and SciMAT were used to answer each scientometric question.

The use of this software, as well as the methodological procedures, are presented in Section 2, while in Section 3 the scientific mapping will be shown and discussed, and Section 4 comprises the concluding thoughts.

Materials and methods

The methodological procedures followed in this paper can be organized in four stages, which are detailed in the sequence:

Stage 1 – Research scope

In the first stage, the research scope was defined as being three areas from renewable energy sources: photovoltaic energy, wind energy, and biomass energy. These three areas were chosen because they account for over 60% of the total jobs generated in renewable energy generation plants (IRENA, 2018). Considering these three areas, the research was structured to respond adequately to the suggested questions and enabled the measurement of research density, pointing out the scientific development of each one of those three renewable energy sources.

Stage 2 – Search filters definition

At this stage, the search filters were defined to enable a search for articles with a potential possibility to respond to the objective of the article. Considering that the objective of this article is to research the development and the density of the research that involves the three types of generation, the search terms were defined as solar or photovoltaic energy, wind energy, and biomass energy. However, after initial tests on the database, it was found that using the terms referring to the three energy sources, the scope of the themes studied is wide, making it difficult to disclose research related to management, performance evaluation, and decision making related to these energy sources. This way, the search term “renewable energy” was added with the Boolean operator AND to refine the searches and to enable the recovery and inclusion of a greater number of articles with an approach aimed at the management of those energy sources considering them as renewable energy sources. Research areas in the Scopus database were limited to “Energy; Engineering; Decision Sciences; Business, Management and Accounting; Economics Econometrics and Finance”, since a relation ship can be established between the technologies used to generate energy through renewable sources and business management through performance evaluation, cost and investment management, business diagnosis and decision making. The first papers published in these areas were from the 1970s, but the period considered was the last 15 years, where a considerable increase in publications occurred only in 2008 and 2009 (see Figure 2 in Section 3.1) with the approval by the European Parlament on April 23, 2009, of the Directive 2019/28/CE (Parlamento Europeu, 2009). So, starting the analysis from 2005 onwards, it was possible to study the themes considering this visible increase in the publications. Those search filters are presented in Table 1.

Search filters.

FilterWind energyPhotovoltaic energyBiomass energy
Document typeArticlesArticlesArticles
Search in Subject áreaTitle, abstract or keywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and FinanceTitle, abstract or keywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and FinanceTitle, abstract or KeywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and Finance
Years2005–20192005–20192005–2019
Search terms“wind energy” AND “renewable energy“solar energy” OR “photovoltaic energy” AND “renewable energy”“biomass energy” AND “renewable energy
Stage 3 – To retrieve articles

In Stage 3 the search in the Scopus database was done, and with the search filters detailed in Table 1, the number of retrieved articles was:

Wind energy: 3,121 articles;

Photovoltaic energy: 4,744 articles;

Biomass energy: 332 articles.

Stage 4 – Analysis

Based on the retrieved articles from Stage 3, Stage 4 was divided into four analyses based on the four questions proposed by Light, Polley, and Börner (2014). To answer these questions it was proposed to use a science mapping analysis developed through the following tasks (Börner, Chen, & Boyack, 2005; Cobo et al., 2011b, 2012):

Preprocessing: data cleaning to eliminate errors and duplicated information such as duplicate articles, names, and other information. This task can occur either directly in the database or in the software used for analysis;

Network extraction: relations between two units, such as words or authors, are established;

Normalization: use of data normalization procedures, as an equivalence index, to enable reliable data analysis (Baierle et al., 2020);

Mapping: application of techniques to build the sciences maps and graphics;

Analysis: discovering useful knowledge from data, networks, and maps (Cobo et al., 2011b), such as node degrees, edges heights, or other measures;

Visualization: representation techniques to show a science map enabling analysis.

These tasks were performed for the development of the analyzes presented in Section 3.

Computational tools used

Microsoft Excel software was used to perform graphics to allow temporal and geographic analysis. Another tool used to answer the research questions was SciMAT, which is a freeware software used for mapping the science, allowing researchers to analyze the intellectual, social and conceptual evolution of a specific scientific field (Cobo et al., 2011a). SciMAT is composed of three modules to develop a science mapping approach: the first to manage entities knowledge, the second to carry out the science mapping analysis, and the third to generate results and maps (Martínez et al., 2015). After importing data in ISI, RIS, or csv format, the SciMAT workflow can be divided into four stages (Cobo et al., 2012; Cobo et al., 2015):

Build the data set: configuration of the periods, the aspects to analyze, and the data to be used;

Create and normalize the network: the network is built using co-occurrence or coupling relations, filtering to show only representative items;

Apply a clustering algorithm to get the map: selection of the clustering algorithm to be used;

Perform a set of analyses: the configuration and execution of the analyses.

Four types of maps that SciM AT generates were used in this research: overlapping maps (presented in Figure 2), evolution maps (presented in Figures 6, 7, and 8), cluster's network and strategic diagrams (presented in Figure 9) (Kipper et al., 2020). With these maps, it is possible to highlight some starting points for other research, such as through the evolution maps, where the researcher can search for a trend based on connections not yet consolidated on the map, or even search for the co-occurrence of words that cannot be explained intuitively. Through the clustering algorithms it is possible to identify groups and subgroups in a research area according to the selected similarity criteria (da Costa et al., 2019). In this paper, the SciMAT period was subdivided into five periods of three years each, providing a punctual analysis of the evolution of the words used and clarifying the evolutionary characteristics of the research.

To research between authors and answer the “Who” question, the Sci2 Tool was used. The Sci2 Tool is a set of computational tools designed to study science (Guler et al., 2016) through network analysis, providing a visualization of datasets at the micro, meso, and macro levels (Light, Polley, & Börner, 2014; Sci2 Tool, 2019). The first analyses involving networks were the studies of citations networks (Newman, 2001a, 2001b), later being developed as maps of authors that help to identify who contributed to the science, homogeneity vs. heterogeneity, cause and effect, speed of science, monitoring and evaluating the evolution of areas for further scientific research (Börner et al., 2012). Author networks are important but do not alone provide enough information to conclude whether an author is relevant in his or her research area (Nara et al., 2019). In the authors’ networks of this article, the names of the authors who centralize the largest research groups in each theme are highlighted, but it should be noted that although smaller, the other networks also have scientific relevance in the topics addressed. In this research, author networks are aggregated with other analyses to obtain a comprehensive understanding of renewable energy research areas.

Results and discussion

In the following, the results obtained from the analysis carried out with the computational tools are presented.

Temporal analysis

The first question answered, “When?”, brought a temporal analysis, as already mentioned in Section 2.2, where the period considered in the searches was the last 15 years, and this choice is justified by the graph shown in Figure 1.

Figure 1

Quantitative temporal analysis.

Figure 2 presents an overlapping map elaborated through SciMAT and adapted to show all pertinent information. This graphic shows the behavior of the number of words related to the three renewable energy sources in each of the following periods: 2005–2007, 2008–2010, 2011–2013, 2014–2016, and 2017–2019. The time was divided in this way to allow an evaluation of the characteristics of the words used.

Figure 2

Overlapping map with the number of related words per period.

From Figure 1, it is clear that photovoltaic energy has the largest number of articles and words, followed by wind energy, and—with a much smaller number—biomass energy. Figure 2 shows the number of words related to each renewable energy source per period. To obtain this comparative analysis of words by period, the metadata of the retrieved articles, excluding the references, were exported from the Scopus database in the “.ris” format and imported into SciMAT. Thus, for each type of renewable energy source researched, an analysis was carried out. Photovoltaic energy has 1,679 words in the first period, rising to 11,943 in the last period, while wind energy has 1,211 in the first period and 7,385 in the last period, and biomass energy has 347 in the first period and 1,360 in the last period. From the arrows directed to the clusters, one also perceives a great aggregation of new words into the research, as well as an exit of words of the searches from the arrows that have the direction exiting the clusters. It is noteworthy that the number of words related to research in photovoltaic energy remained nearly stable in periods two, three, and four, increasing considerably in the last period, while for wind energy research, the number of words increased gradually, and in biomass energy, the number of words in the second, third, and fourth periods remained nearly stable, only increasing in the last period.

This difference in the number of studies may be due to the difference between the systems, especially concerning the region of installation, ease, and minimum requirements. For photovoltaic energy, the direction in which the panels are installed will not prevent the system from generating energy, but will only reduce its efficiency (Daghigh et al., 2011). Solar energy is the most abundant energy on earth, and photovoltaic systems do not need a sunny day to operate, because even on cloudy days they produce electricity, but at a lower intensity than on clear days (Hassan et al., 2020). Many studies have been carried out to develop different materials for the construction of panels, generating less waste and also not allowing the system to overheat (Letcher, 2018; Salameh, New York, & Diego, 2014). From the irradiation incident on the photovoltaic module, only 15 to 20% is converted into electrical energy, hence the great efforts and studies to improve the performance (Fayaz et al., 2019). For wind power to be considered technically feasible, a minimum wind velocity of 7 to 8 m/s is required (Poole et al., 1993). Therefore, wind energy is only economically viable in places where wind conditions are good for wind turbines, making the system still unstable and less attractive than photovoltaic (Ayyarao, 2019). The concept of biomass comprises all organic matter used as energy sources, such as agricultural waste, wood, and plants. Biomass processing is very difficult due to the low calorific value, low bulk density, and high moisture content of raw materials, which seriously limits its use on a large scale (Stolarski et al., 2013; Suarez & Luengo, 2003). These facts may have been decisive, such that studies in this area have not progressed over the years.

Analysis of author networks

In this subsection, author network maps are presented to answer the question “Who” about the terms related to renewable energy sources. In each map of authors’ networks, a threshold was established, mentioned in the description of the figures, related to the size of the node so that the names of the authors with the greatest influence on the researched topics could appear. The first network is shown in Figure 3 and presents the author network for researchers who have more than five articles indexed in the Scopus database with the search terms “renewable energy” and “solar energy” or “photovoltaic energy”, considering also the search filters presented in Table 1. The names of the authors who appear are those with a node degree equal to or higher than 18.

Figure 3

Authors’ networks for photovoltaic energy.

There are nine networks with 17 authors with a node size equal to or greater than 18, and in a single network, there are eight authors in smaller interconnected networks with a node size equal to or greater than 18. Completing the map, another 15 smaller networks can still be viewed. Of those 17 authors, only seven authors have a node size equal to or greater than 25: Cabeza has node size 38, Hodge and Sopian have node size 34, Rosen has node size 33, Akbarzadeh and Kroposki have node size 28 and Duic has node size equal to 27.

The authors Cabeza with node degree 38 and Hodge with node degree 34 are the highest in this map. Cabeza has 12 articles related to the subject of solar or photovoltaic energy, some of them being about thermal energy storage systems (Cabeza et al., 2017; Gibb et al., 2018; Jacob et al., 2016; Peiró et al., 2018; Ruiz-Cabañas et al., 2017), key performance indicators for energy storage systems (Cabeza et al., 2015), and other correlated subjects. Hodge has seven papers indexed in Scopus about high renewable power systems (Du et al., 2018; Du et al., 2019), trends in wind and solar energy forecasting technologies (Hodge et al., 2018; Orwig et al., 2015), estimation of solar irradiance (Zhang et al., 2019), and other related articles. Sopian has 11 papers indexed in Scopus about photovoltaic thermal systems technologies (Nazri et al., 2018; Othman et al., 2013; Rukman et al., 2019), feed-in tariff policy (Bakhtyar et al., 2013; Bakhtyar et al., 2015), solar radiation prediction (Azhari et al., 2008), and other research on correlated themes. Rosen is a researcher with 16 papers on the subjects of exergetic analysis and assessment of different systems (Granovskii, Dincer, & Rosen, 2007; Hacatoglu, Dincer, & Rosen, 2011; Khalid, Dincer, & Rosen, 2015; Rezaie, Reddy, & Rosen, 2018), hybrid renewable energy systems for different applications (Krajačić et al., 2018; Maleki, Rosen, & Pourfayaz, 2017; Soltani et al., 2014; Zhang et al., 2018), and others researches on similar subjects. Akbarzadeh has 10 papers on the subject, almost all about salinity-gradient solar ponds related to electric power generation (Akbarzadeh, Johnson, & Singh, 2009; Alcaraz et al., 2018; Bernad et al., 2013; Leblanc, Andrews, & Akbarzadeh, 2010; Singh et al., 2017; Singh, Tundee, & Akbarzadeh, 2011; Valderrama et al., 2011). Kroposki and his network are linked with the network of Hodge. They have some papers in common such as those about high renewable energy penetrated power systems (Du et al., 2018; Du et al., 2019). The last author with a node degree higher than 25 is Duic with node degree 27. Duic published papers about the integration of solar energy in the energy mix, analyzing the economic and environmental implications (Pfeifer et al., 2019), energy scenarios in Malta (Busuttil, Krajačić, & Duić, 2008) and southeast Europe (Dominković et al., 2016), and other related papers. Among the smaller networks, the networks centralized by Patt, who has four articles on the topic, and the one centralized by Escobar, who has six articles on the subject can be mentioned. The network centralized by Patt researched on risks involving renewable energy projects (Komendantova et al., 2012), barriers for the expansion of electricity grids (Battaglini et al., 2012), and other related papers, while the network centralized by Escobar researched about the influence of solar energy resource assessment uncertainty in the levelized electricity cost of concentrated solar power plants (Hanel & Escobar, 2013), the impact of concentrated solar power in electric power systems (Mena et al., 2019), and other related papers. When analyzing the publications from the main and some minor research networks on the subject of photovoltaic energy, it can be said that some specific studies are dealing with aspects of management and performance evaluation of this energy source, but no articles were found related to the elaboration of modeling for decision-making in these major research networks.

The next map of author networks, presented in Figure 4, is about wind energy. The author names shown are those with a node degree equal to or higher than 17.

Figure 4

Authors’ networks for wind energy.

There are seven author networks on the subject of wind energy with at least one author with a node greater than or equal to 17, and six authors have a node greater than or equal to 23, indicating them as the most relevant in research related to renewable energy coming from wind: Catalão with node degree 28, Breyer and Arroyo with node degree 26, Mañana with node degree 24, Senjyu and Wang with node degree 23. Completing the map, another 23 complementary networks can still be viewed.

Catalão is a researcher with 12 papers, and he leads a network in research such as Monte Carlo simulation for optimal operation in electrical systems with high renewable sources integration (Osório et al., 2015), storage of wind energy surplus (Rodrigues et al., 2015), integration of wind energy with other renewable energy sources through a model using a stochastic heuristic Multi-Objective Multi-Criteria Decision Making method (Hajibandeh et al., 2018), and other papers on related subjects. Breyer is a researcher who has published 10 papers on the subject of wind energy as scenarios for sustainable energy in Scotland (Child et al., 2019), the Aland Islands (Child, Nordling, & Breyer, 2017), and the Berlin-Brandenburg region, Germany, using a cost optimization approach (Moeller et al., 2014) and others. Arroyo has 10 papers on the subject and Mañana has nine papers on the subject, and they are the centralized nodes from a network. These authors study together the subject of CO2 footprint reduction and efficiency increase using a dynamic rating of the electricity grid (Arroyo et al., 2018), the increase of grid integration of wind energy using ampacity techniques (Madrazo et al., 2013), and the analysis of the ampacity management in a 132 kV overhead line placed in a high-wind generation area (Madrazo et al., 2015), and other related papers. Senjyu is at the center of his network and has eight papers on the subject, including research to present an output power leveling control strategy for wind farms (Sakamoto et al., 2008; Senjyu et al., 2006), to propose a methodology for solving the generation planning problem for thermal units with wind and solar energy systems (Chakraborty et al., 2009), and other related papers. Wang has seven papers and is the center of his author network, with papers related to wind speed forecasting using several methods such as a back propagation Neural Network (Ren et al., 2014), Extreme Learning Machine, Ljung Box Q-test, and Seasonal Auto-Regressive Integrated Moving Average (Wang et al., 2015), multi-objective optimization (Yang et al., 2019), and other papers with hybrid proposals. Among the 23 complementary networks, it can be mentioned the networks centralized by Ilinca, with node degree 13, has five articles on the subject, and Fagiano, with node degree 9, has articles on the subject. The network centralized by Ilinca proposed a computer model for financial, environmental and risk analysis of a hybrid wind-diesel system with compressed air energy storage (Benchaabane et al., 2019), measured robustness as a tool to address strategic wind farm issues through multicriteria decision-making methods (Vazquez, Waaub, & Ilinca, 2013), among other research. The network centralized by Fagiano research on themes such as the problem of launching a tethered rigid aircraft for airborne wind energy generation (Fagiano & Schnez, 2017), the high-altitude wind power generation (Fagiano, Milanese, & Piga, 2010), and other related research. Among the main and in the complementary author networks, there is a trend of research aimed at technologies related to the production of wind energy, the materials used, wind speed forecasting, prediction of energy scenarios in different locations, or aspects related to the deployment and production cost of this energy. From this analysis, it can be said that only one of the main research networks in this theme has articles published with some relation to the management of wind energy installations.

Figure 5 presents the author network map for biomass energy as a renewable energy source, and the names that appear are authors with node degrees equal to or higher than 8.

Figure 5

Authors’ networks for biomass energy.

From the elaborated map, we can see that few research networks operate in the area of biomass energy as a source of renewable electricity generation. There are three networks where there is at least one author with a node equal to or greater than 8, and only two have authors with a node greater than or equal to 10: these authors are Tokimatsu with node size 10, and the authors of the same network Wahab, Mohamed, Rasat, Amini, and Ahmad, all with node equal to 10. Completing the map, another 25 complementary networks can still be viewed.

Tokimatsu has two papers on the subject that investigate people's understanding of biomass energy (Biddinika et al., 2017), and global zero-emissions scenarios (Tokimatsu et al., 2016). The other five authors in the same network, Wahab, Mohamed, Rasat, Amini, and Ahmad, published two papers together, one about palm oil (Sharizal Sirrajudin et al., 2016), and the one about wild Leucaena Leucocephala species (Rasat et al., 2016) as a biomass energy source. Among the re searches carried out by the other networks of authors, those of the network of Shahbaz which researched about the environmental Kuznets curve for CO2 emissions in 11 countries (Sinha, Shahbaz, & Balsalobre, 2017), the specification of the environmental Kuznets curve for the US economy by accounting for the presence of a major renewable energy source and trade openness (Shahbaz et al., 2017). It can be noticed that the author networks have articles about biomass as a source of renewable energy, but these research works are not directed at the transformation of biomass into electricity.

Analysis of word occurrences and co-occurrences

This subsection was elaborated to answer the question “What?”, and to do this, the SciMAT software was used. The year range used in Figures 6, 7, and 8 was divided into the same five periods used in Figure 2 (2005–2007, 2008–2010, 2011–2013, 2014–2016, and 2017–2019). For Figures 6, 7, 8, and 9, a minimum number of words’ occurrences was used, making it possible to adequately represent the main words and relationships between them in each period. Table 2 shows these minimum numbers of occurrences. The other steps of the analyzes carried out in SciMAT were the following: (i) unit of analysis: words; (ii) kind of matrix: co-occurrence; (iii) normalization measure: equivalence index; (iv) clustering algorithm: simple centers algorithm; (v) document mappers: core mapper; (vi) quality measures: h-index, g-index, q2-index, hg-index, and average citations; (vii) measures for the longitudinal map: equivalence index.

Figure 6

Words map for photovoltaic energy.

Figure 7

Words map for wind energy.

Figure 8

Word map for biomass energy.

Figure 9

Cluster's network and strategic diagrams about photovoltaic, wind, and biomass energy.

Minimum frequency of words occurrences.

PeriodWind energyPhotovoltaic energyBiomass energy
2005–2007343
2008–2010573
2011–2013573
2014–2016583
2017–20196104

In evolution maps shown in Figures 6, 7, and 8 there are two types of lines connecting the circles of different periods: the continuous lines indicate a strong and direct connection between the words, while the dashed lines indicate the weaker relationship trends between words. In this way, the word analysis starts with Figure 6, which shows the words map for photovoltaic energy.

In the periods analyzed, renewable resources and solar energy were the strongest words that appear, also having a strong link between themselves. These words also have a connection with the words or terms: alternative energy; electric generators; energy conservation; solar radiation; electricity; renewable energy resources; and energy storage. In the development of these research works, new words appear, such as costs strongly linked to renewable resources (Haseeb et al., 2019) and investments. Solar radiation is strongly connected with forecasting (Hodge et al., 2018; Orwig et al., 2015), energy storage linked to solar concentrators (Cabeza et al., 2017; Gibb et al., 2018; Jacob et al., 2016; Peiró et al., 2018; Ruiz-Cabañas et al., 2017). Other strong connections are between: energy conservation and energy utilization; energy storage and heat storage; optimization and energy resources; and photovoltaic effects and optimization. Most of the aforementioned co-occurrences can be explained intuitively, but among the less intuitive ones, which can be further explored, the connection between the photovoltaic effects concept and optimization techniques stands out, which may be an interesting research gap. In this map, only the words costs and investments give an idea that there are some articles related to management and decision-making and photovoltaic energy. It can be said that studies concerning the management, decision-making, and performance evaluation of this renewable energy source are attracting more researchers since they have become important to support the correct direction of the investments and the technology implemented.

Figure 7 presents the word relations for the fifth period for wind energy research.

In this figure, it can be seen that wind power and renewable resources were the most important terms. Besides, there is a trend in periods one and two of talking about wind power and electricity as energy resources, then in period three there begins to be a concern with renewable resources, carbon dioxide, sustainable development (Hu et al., 2017) and costs (Haseeb et al., 2019). In periods four and five there is a shift to discussing technologies such as DC converters, permanent magnets, and wind turbines. In periods four and five there appear the strongest connections between: forecasting and speed; permanent magnets and energy conversion; and carbon dioxide and greenhouse gases. Also, in these periods there begins to be a concern with investments (Martí-Ballester, 2019), energy management, costs, and decision-making. This reveals that over the last ten years, research has been deepening and considering more terms and more of the variables that are part of a system or plant for renewable energy generation. The advancement of studies of the variables involved gives rise to the need for management and control of these variables, and in that sense, the decision-making (Hajibandeh et al., 2018) term only appears in the last period, proving that there is a research gap that is only in the last few years beginning to be explored.

Even with less research about biomass as a renewable energy source, a words map was elaborated to show the most important word on this subject. Figure 8 shows this map.

Beginning with the terms of fossil fuels and renewable energy resources, the strongest links were in the sequence with biological materials. However, in the second period, biomass energy and bioenergy have a strong link with renewable resources and renewable energy resources from the third period. Renewable resources from the third period link strongly with renewable energy resources and biomass energy and bioenergy. In the last period the terms carbon dioxide, wind power, renewable energy, and alternative energy appear with relevant links with the fourth period. Considering these word maps, there are no words or terms related to energy management, performance evaluation, or decision-making.

Figure 9 shows two kinds of maps: the cluster's networks and strategic diagrams. On the left side are the cluster's networks elaborated to give an understanding of what words are linked to the centralizing term on each renewable energy source researched, showing how strong the relations are between the terms related to the principal term in the most recent period (2017 to 2019). On the right side, the strategic diagrams are presented to show which keywords have greater centrality and density among the retrieved articles. The terms on the right are those that can be considered more central in most of the research, and the more upward the more densely searched. Therefore, those terms represented in the first quadrant can be considered the most relevant for each area in question.

As shown in the figure, from 2017 until 2019, the centralizing term on photovoltaic energy was “solar-energy”, which also appeared 1,201 times according to the strategic diagram. The words and terms with stronger links with solar energy and that represent the consolidation and importance of research in this area are solar power, solar power generation, renewable energies, and photovoltaic systems. There is also a strong link between alternative energy and solar power. The words and terms with thinner connection edges present some tendencies and possible gaps in this area: energy policy, energy efficiency, energy storage, photovoltaic cells, and alternative energy. From the strategic diagram, it can be seen that the other words which are densely researched are heat-storage, greenhouse-gases, heating, and renewable resources. Considering this analysis, there is a research opportunity regarding energy management and performance evaluation, since words or terms that indicate these study fields do not appear in this figure.

Related to wind energy, the term “wind power”, with density 1.0 as the central term of the map, and also with 679 mentions according to the strategic diagram, is linked consistently to the terms renewable energies, wind energy, wind turbines, and alternative energy, proving the consistency and development of these themes in the current period. However, the lower-intensity connections, which show an area of research that can be strengthened, refer to terms such as renewable resource, electric power generation, renewable energy, solar energy, energy policy, renewable energy source, wind, optimization, and electric power generation. Another central and densely researched terms in this theme are greenhouse-gases, energy storage, and energy resources. Among all these term s, energy policy may give an idea in the direction of the gap suggested in this article, since good management of companies is necessary to be able to comply with legislation and policies in the energy sector.

Related to biomass energy, the centralizing term with density 0.78 between 2017 to 2019 was “carbon-dioxide”, which appears 44 times according to the strategic diagram. The term carbon dioxide binds strongly to the term biomass. The term biomass, although it appears in numerous articles, does not have a strong connection with other terms, which also explains why the term is not at the center of the diagram, just as it doesn’t appear in the strategic diagram. Other strong links a re between carbon emission and biomass power, and greenhouse gases and gas emissions, which can be intuitively explained. However, in this diagram, there are no terms or links between terms that lead to an indication of studies related to the management of biomass as a renewable source of electricity.

The word connections presented by SciMAT indicate research trends, which way they are going, and how this can generate new research opportunities. These research opportunities may focus on the most interesting word co-occurrences presented in this article, aiming to deepen the study and to analyze the cause and the specific meaning of these co-occurrences.

Geographic analysis

Figure 10 presents a graphic with the 25 countries/regions with the most publications on renewable energy sources, showing the relations in these publications among the three sources: photovoltaic, wind, and biomass.

Figure 10

Number of papers published per coutry/region.

Comparing this graphic of the Coutry/region publishing the most research and studies, as confirmed daily, the three countries that lead these studies and research (United States, China, and India) are also the three countries that generate the most jobs in the renewable energy generation plants (IRENA, 2018). Countries in Africa and the Middle East hardly appear at all because, according to IRENA (Whiteman et al., 2018), they contributed only about 3% of the total renewable energy capacity in 2017.

The origins of the most influential authors in the presented networks are as follows:

Photovoltaic energy area: Cabeza is from Spain, Hodge and Kroposki are Americans, Rosen is a Canadian researcher, Sopian is from Malaysia, Akbarzadeh is Australian, and Duic is from Croatia.

Wind energy: Catalão is a Portuguese researcher, Breyer is Finnish, Arroyo and Mañana are Spanish, Senjyu is Japanese, and Wang is Chinese.

Biomass energy: Tokimatsu is a Japanese researcher, while Wahab, Mohamed, Rasat, Amini, and Ahmad are from Malaysia.

This cross-checking shows that the countries/regions that have the most publications in these areas are also the source of most of the most relevant authors on the topics. It can be highlighted that, although the United States, China, and India are the top three countries in the number of publications, only three from the 19 most influential authors are from these countries. It can be understood from this that the research on these three sources of renewable energy is widespread and developed in several countries/regions and by several research groups, not being concentrated only on the main research networks. Only Croatia and Finland among the countries/regions of origin of the main authors of the most influential research networks do not appear among the 25 countries/regions with the most publications in the research areas.

Concluding thoughts

The results and discussion section presented temporal, author networks, word co-occurrences, and geographic approaches to study scientific development on three renewable energy sources: photovoltaic, wind, and biomass. From these analyses, it can be summarized that the research has shown a considerable development leap from 2008 until reaching its apex in the current period. Several networks of authors are involved in research related to the development of the technologies themselves, many of these networks with links to each other, showing an exchange of knowledge and technology. Besides, the main author networks are the origin of a wide range of articles related to renewable energy technologies, but just a few articles by these authors deal with the management, performance evaluation, and decision-making related to the sources of renewable energy. There are a large number of related words signifying a scientific evolution in broad directions; however, the most commonly used words and terms refer to the renewable energy sources themselves, as well as the technologies used to generate electricity from these sources. Terms and words regarding management, performance evaluation, and decision-making related to these sources only appear occasionally in the period from 2017 to the current one. The United States, China, and India are the three countries producing the most research, and they are also the three countries with major jobs generation in installations of renewable energy sources, and research spreading to countries across continents.

Therefore, considering the scientometric analysis developed, it can be concluded that the purpose of this paper was achieved, since the scientific development of renewable energy sources was mapped, pointing out the principal words, author networks, and countries/regions that produce the most research in the area of photovoltaic energy, wind energy, and biomass energy. From this mapping, it can be said that there is a trend and some interesting opportunities for studies about management, performance evaluation, and decision-making regarding renewable energy sources. Although articles are coming from some of the main networks of authors and the term “decision-making” appears as a trend in the period from 2017 to 2019, considering the broad scope, research in this area has not yet been consolidated.

Among the computational tools used, the Sci2 Tool provides those who use it with a view of who are the main research groups on certain topics, enabling researchers to develop studies based on already consolidated research. Besides, when visualizing the networks, the researcher can study approaches and readings on a topic, considering different views coming from different research networks. However, there are disadvantages in visualization in the format of networks, as sometimes good research carried out in a more isolated way can be omitted, since smaller networks or individual authors are not privileged in the visualization of networks. Among the maps elaborated through SciMAT, the evolution map brings a view of the trends in a research theme over time, while the view of the centralizing theme in a period shows the most prominent theme in that period. As in the Sci2 Tool, in SciMAT interesting words or terms for research can be omitted because they are not used so often that they appear on maps. It is worth mentioning that both the Sci2 Tool and SciMAT allow other types of analysis not used in this article. The results presented with the aid of the Sci2 Tool and SciMAT in this article are not absolute, but they show possible directions and trends in research based on previously obtained data.

From an academic point of view, this article indicates research opportunities on themes related to management, studies on costs and investments, systemic diagnosis, and performance evaluation for assertive decision-making in businesses on renewable energy sources. Besides, the methodology and tools used in this paper can be applied to other domains and research, guiding new researchers to areas of research that have not yet been explored or little explored, increasing interest and bringing more and more relevance to the academic world.

For companies, this article shows that there is a tendency to implement business management in businesses related to renewable energy sources at different levels, including performance evaluation, aiming at higher performance, more assertive decision making, and adequate use of technical and financial resources. To corroborate these findings, data from world agencies can be used, since they analyze the global production of renewable energies, showing exactly what the research indicates, which is an area that is still growing and developing. Currently, it employs around 10,000,000 people worldwide and is projected to grow exponentially by 2050 (IRENA, 2018), making the management of these systems indispensable.

Limitations and future research

This paper was limited to the articles indexed in the Scopus database. Another limitation was the broad scope of the discussions that were developed to analyze the scientific production as a whole, without explanations about how each renewable energy works. As future research, we leave a suggestion to analyze the scientific productions that address the issues related to management and decision-making in renewable energy generation.

Figure 1

Quantitative temporal analysis.
Quantitative temporal analysis.

Figure 2

Overlapping map with the number of related words per period.
Overlapping map with the number of related words per period.

Figure 3

Authors’ networks for photovoltaic energy.
Authors’ networks for photovoltaic energy.

Figure 4

Authors’ networks for wind energy.
Authors’ networks for wind energy.

Figure 5

Authors’ networks for biomass energy.
Authors’ networks for biomass energy.

Figure 6

Words map for photovoltaic energy.
Words map for photovoltaic energy.

Figure 7

Words map for wind energy.
Words map for wind energy.

Figure 8

Word map for biomass energy.
Word map for biomass energy.

Figure 9

Cluster's network and strategic diagrams about photovoltaic, wind, and biomass energy.
Cluster's network and strategic diagrams about photovoltaic, wind, and biomass energy.

Figure 10

Number of papers published per coutry/region.
Number of papers published per coutry/region.

Search filters.

FilterWind energyPhotovoltaic energyBiomass energy
Document typeArticlesArticlesArticles
Search in Subject áreaTitle, abstract or keywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and FinanceTitle, abstract or keywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and FinanceTitle, abstract or KeywordsEnergy; Engineering; Decision Sciences; Business, Management and Accounting; Economics, Econometrics and Finance
Years2005–20192005–20192005–2019
Search terms“wind energy” AND “renewable energy“solar energy” OR “photovoltaic energy” AND “renewable energy”“biomass energy” AND “renewable energy

Minimum frequency of words occurrences.

PeriodWind energyPhotovoltaic energyBiomass energy
2005–2007343
2008–2010573
2011–2013573
2014–2016583
2017–20196104

Abramo, G. (2018). Revisiting the scientometric conceptualization of impact and its measurement. Journal of Informetrics, 12(3), 590–597. https://doi.org/10.1016/j.joi.2018.05.001AbramoG.2018Revisiting the scientometric conceptualization of impact and its measurementJournal of Informetrics123590597https://doi.org/10.1016/j.joi.2018.05.00110.1016/j.joi.2018.05.001Search in Google Scholar

Akbarzadeh, A., Johnson, P., & Singh, R. (2009). Examining potential benefits of combining a chimney with a salinity gradient solar pond for production of power in salt affected areas. Solar Energy, 83(8), 1345–1359. https://doi.org/10.1016/j.solener.2009.02.010AkbarzadehA.JohnsonP.SinghR.2009Examining potential benefits of combining a chimney with a salinity gradient solar pond for production of power in salt affected areasSolar Energy83813451359https://doi.org/10.1016/j.solener.2009.02.01010.1016/j.solener.2009.02.010Search in Google Scholar

Alcaraz, A., Montalà, M., Cortina, J.L., Akbarzadeh, A., Aladjem, C., Farran, A., & Valderrama, C. (2018). Design, construction, and operation of the first industrial salinity-gradient solar pond in Europe: An efficiency analysis perspective. Solar Energy, 164, 316–326. https://doi.org/10.1016/j.solener.2018.02.053AlcarazA.MontalàM.CortinaJ.L.AkbarzadehA.AladjemC.FarranA.ValderramaC.2018Design, construction, and operation of the first industrial salinity-gradient solar pond in Europe: An efficiency analysis perspectiveSolar Energy164316326https://doi.org/10.1016/j.solener.2018.02.05310.1016/j.solener.2018.02.053Search in Google Scholar

Arroyo, A., Castro, P., Manana, M., Domingo, R., & Laso, A. (2018). CO2 footprint reduction and efficiency increase using the dynamic rate in overhead power lines connected to wind farms. Applied Thermal Engineering, 130, 1156–1162. https://doi.org/10.1016/j.applthermaleng.2017.11.095ArroyoA.CastroP.MananaM.DomingoR.LasoA.2018CO2 footprint reduction and efficiency increase using the dynamic rate in overhead power lines connected to wind farmsApplied Thermal Engineering13011561162https://doi.org/10.1016/j.applthermaleng.2017.11.09510.1016/j.applthermaleng.2017.11.095Search in Google Scholar

Ayyarao, T.S.L.V. (2019). Modified vector controlled DFIG wind energy system based on barrier function adaptive sliding mode control. Protection and Control of Modern Power Systems, 4(1), 1–8. https://doi.org/10.1186/s41601-019-0119-3AyyaraoT.S.L.V.2019Modified vector controlled DFIG wind energy system based on barrier function adaptive sliding mode controlProtection and Control of Modern Power Systems4118https://doi.org/10.1186/s41601-019-0119-310.1186/s41601-019-0119-3Search in Google Scholar

Azhari, A.W., Sopian, K., Zaharim, A., & Al Ghoul, M. (2008). A new approach for predicting solar radiation in tropical environment using satellite images – Case study of Malaysia. WSEAS Transactions on Environment and Development, 4(4), 373–378.AzhariA.W.SopianK.ZaharimA.Al GhoulM.2008A new approach for predicting solar radiation in tropical environment using satellite images – Case study of MalaysiaWSEAS Transactions on Environment and Development44373378Search in Google Scholar

Baierle, I.C., Schaefer, J.L., Sellitto, M.A., Fava, L.P., Furtado, J.C., & Nara, E.O.B. (2020). Moona software for survey classification and evaluation of criteria to support decision-making for properties portfolio. International Journal of Strategic Property Management, 24(4), 226–236. https://doi.org/10.3846/ijspm.2020.12338BaierleI.C.SchaeferJ.L.SellittoM.A.FavaL.P.FurtadoJ.C.NaraE.O.B.2020Moona software for survey classification and evaluation of criteria to support decision-making for properties portfolioInternational Journal of Strategic Property Management244226236https://doi.org/10.3846/ijspm.2020.1233810.3846/ijspm.2020.12338Search in Google Scholar

Bakhtyar, B., Saadatian, O., Alghoul, M.A., Ibrahim, Y., & Sopian, K. (2015). Solar electricity market in Malaysia: A review of feed-in tariff policy. Environmental Progress and Sustainable Energy, 34(2), 600–606. https://doi.org/10.1002/ep.12023BakhtyarB.SaadatianO.AlghoulM.A.IbrahimY.SopianK.2015Solar electricity market in Malaysia: A review of feed-in tariff policyEnvironmental Progress and Sustainable Energy342600606https://doi.org/10.1002/ep.1202310.1002/ep.12023Search in Google Scholar

Bakhtyar, B., Sopian, K., Zaharim, A., Salleh, E., & Lim, C.H. (2013). Potentials and challenges in implementing feed-in tariff policy in Indonesia and the Philippines. Energy Policy, 60, 418–423. https://doi.org/10.1016/j.enpol.2013.05.034BakhtyarB.SopianK.ZaharimA.SallehE.LimC.H.2013Potentials and challenges in implementing feed-in tariff policy in Indonesia and the PhilippinesEnergy Policy60418423https://doi.org/10.1016/j.enpol.2013.05.03410.1016/j.enpol.2013.05.034Search in Google Scholar

Battaglini, A., Komendantova, N., Brtnik, P., & Patt, A. (2012). Perception of barriers for expansion of electricity grids in the European Union. Energy Policy, 47, 254–259. https://doi.org/10.1016/j.enpol.2012.04.065BattagliniA.KomendantovaN.BrtnikP.PattA.2012Perception of barriers for expansion of electricity grids in the European UnionEnergy Policy47254259https://doi.org/10.1016/j.enpol.2012.04.06510.1016/j.enpol.2012.04.065Search in Google Scholar

Batty, M., & Gleeson, B. (2003). The geography of scientific citation + The Difference that Planning Makes. Environment and Planning A, 35, 761–770. https://doi.org/10.1068/a3505comBattyM.GleesonB.2003The geography of scientific citation + The Difference that Planning MakesEnvironment and Planning A35761770https://doi.org/10.1068/a3505com10.1068/a3505comSearch in Google Scholar

Ben Jebli, M., Ben Youssef, S., & Apergis, N. (2019). The dynamic linkage between renewable energy, tourism, CO2 emissions, economic growth, foreign direct investment, and trade. Latin American Economic Review, 28, 2. https://doi.org/10.1186/s40503-019-0063-7Ben JebliM.Ben YoussefS.ApergisN.2019The dynamic linkage between renewable energy, tourism, CO2 emissions, economic growth, foreign direct investment, and tradeLatin American Economic Review282https://doi.org/10.1186/s40503-019-0063-710.1186/s40503-019-0063-7Search in Google Scholar

Benchaabane, Y., Silva, R.E., Ibrahim, H., Ilinca, A., Chandra, A., & Rousse, D.R. (2019). Computer Model for Financial, Environmental and Risk Analysis of a Wind–Diesel Hybrid System with Compressed Air Energy Storage. Energies, 12(21), 4054. https://doi.org/10.3390/en12214054BenchaabaneY.SilvaR.E.IbrahimH.IlincaA.ChandraA.RousseD.R.2019Computer Model for Financial, Environmental and Risk Analysis of a Wind–Diesel Hybrid System with Compressed Air Energy StorageEnergies12214054https://doi.org/10.3390/en1221405410.3390/en12214054Search in Google Scholar

Bernad, F., Casas, S., Gibert, O., Akbarzadeh, A., Cortina, J.L., & Valderrama, C. (2013). Salinity gradient solar pond: Validation and simulation model. Solar Energy, 98(Part C), 366–374. https://doi.org/10.1016/j.solener.2013.10.004BernadF.CasasS.GibertO.AkbarzadehA.CortinaJ.L.ValderramaC.2013Salinity gradient solar pond: Validation and simulation modelSolar Energy98Part C366374https://doi.org/10.1016/j.solener.2013.10.00410.1016/j.solener.2013.10.004Search in Google Scholar

Biddinika, M.K., Diponegoro, A.M., Ali, R.M., Rosyadi, R.I., Tokimatsu, K., & Takahashi, F. (2017). Survey on readability of online information for upgrading understandability of biomass energy technology. Journal of Material Cycles and Waste Management, 19(3), 1069–1076. https://doi.org/10.1007/s10163-017-0596-2BiddinikaM.K.DiponegoroA.M.AliR.M.RosyadiR.I.TokimatsuK.TakahashiF.2017Survey on readability of online information for upgrading understandability of biomass energy technologyJournal of Material Cycles and Waste Management19310691076https://doi.org/10.1007/s10163-017-0596-210.1007/s10163-017-0596-2Search in Google Scholar

Börner, K., Chen, C., & Boyack, K.W. (2005). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255. https://doi.org/10.1002/aris.1440370106BörnerK.ChenC.BoyackK.W.2005Visualizing knowledge domainsAnnual Review of Information Science and Technology371179255https://doi.org/10.1002/aris.144037010610.1002/aris.1440370106Search in Google Scholar

Börner, K., Klavans, R., Patek, M., Zoss, A.M., Biberstine, J.R., Light, R.P., Larivière, V., & Boyack, K.W. (2012). Design and update of a classification system: The ucsd map of science. PLoS ONE, 7(7), e39464. https://doi.org/10.1371/journal.pone.0039464BörnerK.KlavansR.PatekM.ZossA.M.BiberstineJ.R.LightR.P.LarivièreV.BoyackK.W.2012Design and update of a classification system: The ucsd map of sciencePLoS ONE77e39464https://doi.org/10.1371/journal.pone.003946410.1371/journal.pone.0039464339564322808037Search in Google Scholar

Busuttil, A., Krajačić, G., & Duić, N. (2008). Energy scenarios for Malta. International Journal of Hydrogen Energy, 33(16), 4235–4246. https://doi.org/10.1016/j.ijhydene.2008.06.010BusuttilA.KrajačićG.DuićN.2008Energy scenarios for MaltaInternational Journal of Hydrogen Energy331642354246https://doi.org/10.1016/j.ijhydene.2008.06.01010.1016/j.ijhydene.2008.06.010Search in Google Scholar

Cabeza, L.F., Galindo, E., Prieto, C., Barreneche, C., & Inés Fernández, A. (2015). Key performance indicators in thermal energy storage: Survey and assessment. Renewable Energy, 83, 820–827. https://doi.org/10.1016/j.renene.2015.05.019CabezaL.F.GalindoE.PrietoC.BarrenecheC.Inés FernándezA.2015Key performance indicators in thermal energy storage: Survey and assessmentRenewable Energy83820827https://doi.org/10.1016/j.renene.2015.05.01910.1016/j.renene.2015.05.019Search in Google Scholar

Cabeza, L.F., Solé, A., Fontanet, X., Barreneche, C., Jové, A., Gallas, M., Prieto, C., & Fernández, A.I. (2017). Thermochemical energy storage by consecutive reactions for higher efficient concentrated solar power plants (CSP): Proof of concept. Applied Energy, 185(Part 1), 836–845. https://doi.org/10.1016/j.apenergy.2016.10.093CabezaL.F.SoléA.FontanetX.BarrenecheC.JovéA.GallasM.PrietoC.FernándezA.I.2017Thermochemical energy storage by consecutive reactions for higher efficient concentrated solar power plants (CSP): Proof of conceptApplied Energy185Part 1836845https://doi.org/10.1016/j.apenergy.2016.10.09310.1016/j.apenergy.2016.10.093Search in Google Scholar

Carrington, P., Scott, J., & Wasserman, S. (2005). Models and methods in social network analysis. Cambridge University Press. https://books.google.com.br/books?hl=pt-BR&lr=&id=4Ty5xP_KcpAC&oi=fnd&pg=PR9&dq=%22Models+and+methods+in+social+network+analysis%22&ots=9NJLv7tbJ3&sig=nBeqcDbBSs5PmezJX3DaVorpS00CarringtonP.ScottJ.WassermanS.2005Models and methods in social network analysisCambridge University Presshttps://books.google.com.br/books?hl=pt-BR&lr=&id=4Ty5xP_KcpAC&oi=fnd&pg=PR9&dq=%22Models+and+methods+in+social+network+analysis%22&ots=9NJLv7tbJ3&sig=nBeqcDbBSs5PmezJX3DaVorpS0010.1017/CBO9780511811395Search in Google Scholar

Chakraborty, S., Senjyu, T., Saber, A.Y., Yona, A., & Funabashi, T. (2009). Optimal thermal unit commitment integrated with renewable energy sources using advanced particle swarm optimization. IEEJ Transactions on Electrical and Electronic Engineering, 4(5), 609–617. https://doi.org/10.1002/tee.20453ChakrabortyS.SenjyuT.SaberA.Y.YonaA.FunabashiT.2009Optimal thermal unit commitment integrated with renewable energy sources using advanced particle swarm optimizationIEEJ Transactions on Electrical and Electronic Engineering45609617https://doi.org/10.1002/tee.2045310.1002/tee.20453Search in Google Scholar

Chel, A., & Kaushik, G. (2018). Renewable energy technologies for sustainable development of energy efficient building. Alexandria Engineering Journal, 57(2), 655–669. https://doi.org/10.1016/j.aej.2017.02.027ChelA.KaushikG.2018Renewable energy technologies for sustainable development of energy efficient buildingAlexandria Engineering Journal572655669https://doi.org/10.1016/j.aej.2017.02.02710.1016/j.aej.2017.02.027Search in Google Scholar

Chen, C., Li, Y., Song, J., Yang, Z., Kuang, Y., Hitz, E., Jia, C., Gong, A., Jiang, F., Zhu, J.Y., Yang, B., Xie, J., & Hu, L. (2017). Highly Flexible and Efficient Solar Steam Generation Device. Advanced Materials, 29(30), 1701756. https://doi.org/10.1002/adma.201701756ChenC.LiY.SongJ.YangZ.KuangY.HitzE.JiaC.GongA.JiangF.ZhuJ.Y.YangB.XieJ.HuL.2017Highly Flexible and Efficient Solar Steam Generation DeviceAdvanced Materials29301701756. https://doi.org/10.1002/adma.20170175610.1002/adma.20170175628605077Search in Google Scholar

Child, M., Ilonen, R., Vavilov, M., Kolehmainen, M., & Breyer, C. (2019). Scenarios for sustainable energy in Scotland. Wind Energy, 22(5), 666–684. https://doi.org/10.1002/we.2314ChildM.IlonenR.VavilovM.KolehmainenM.BreyerC.2019Scenarios for sustainable energy in ScotlandWind Energy225666684https://doi.org/10.1002/we.231410.1002/we.2314Search in Google Scholar

Child, M., Nordling, A., & Breyer, C. (2017). Scenarios for a sustainable energy system in the Åland Islands in 2030. Energy Conversion and Management, 137, 49–60. https://doi.org/10.1016/j.enconman.2017.01.039ChildM.NordlingA.BreyerC.2017Scenarios for a sustainable energy system in the Åland Islands in 2030Energy Conversion and Management1374960https://doi.org/10.1016/j.enconman.2017.01.03910.1016/j.enconman.2017.01.039Search in Google Scholar

Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2011a). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002CoboM.J.López-HerreraA.G.Herrera-ViedmaE.HerreraF.2011aAn approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory fieldJournal of Informetrics51146166https://doi.org/10.1016/j.joi.2010.10.00210.1016/j.joi.2010.10.002Search in Google Scholar

Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2011b). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525CoboM.J.López-HerreraA.G.Herrera-ViedmaE.HerreraF.2011bScience mapping software tools: Review, analysis, and cooperative study among toolsJournal of the American Society for Information Science and Technology62713821402https://doi.org/10.1002/asi.2152510.1002/asi.21525Search in Google Scholar

Cobo, M.J., Lõpez-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630. https://doi.org/10.1002/asi.22688CoboM.J.Lõpez-HerreraA.G.Herrera-ViedmaE.HerreraF.2012SciMAT: A new science mapping analysis software toolJournal of the American Society for Information Science and Technology63816091630https://doi.org/10.1002/asi.2268810.1002/asi.22688Search in Google Scholar

Cobo, M.J., Martínez, M.A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at Knowledge-Based Systems: A bibliometric analysis. Knowledge-Based Systems, 80, 3–13. https://doi.org/10.1016/j.knosys.2014.12.035CoboM.J.MartínezM.A.Gutiérrez-SalcedoM.FujitaH.Herrera-ViedmaE.201525 years at Knowledge-Based Systems: A bibliometric analysisKnowledge-Based Systems80313https://doi.org/10.1016/j.knosys.2014.12.03510.1016/j.knosys.2014.12.035Search in Google Scholar

Cook, D., & Holder, L. (2006). Mining graph data. John Wiley and Sons Inc. https://books.google.com.br/books?hl=pt-BR&lr=&id=bHGy0_H0g8QC&oi=fnd&pg=PR7&dq=cook+%22Mining+graph+data%22&ots=FtWbVNf0hQ&sig=H3zgSQPkN4YwbubpOy7kBjiKvTUCookD.HolderL.2006Mining graph dataJohn Wiley and Sons Inchttps://books.google.com.br/books?hl=pt-BR&lr=&id=bHGy0_H0g8QC&oi=fnd&pg=PR7&dq=cook+%22Mining+graph+data%22&ots=FtWbVNf0hQ&sig=H3zgSQPkN4YwbubpOy7kBjiKvTU10.1002/0470073047Search in Google Scholar

Da Costa, M.B., Dos Santos, L.M.A.L., Schaefer, J.L., Baierle, I.C., & Nara, E.O.B. (2019). Industry 4.0 technologies basic network identification. Scientometrics, 121(2), 977–994. https://doi.org/10.1007/s11192-019-03216-7Da CostaM.B.Dos SantosL.M.A.L.SchaeferJ.L.BaierleI.C.NaraE.O.B.2019Industry 4.0 technologies basic network identificationScientometrics1212977994https://doi.org/10.1007/s11192-019-03216-710.1007/s11192-019-03216-7Search in Google Scholar

Daghigh, R., Ibrahim, A., Jin, G.L., Ruslan, M.H., & Sopian, K. (2011). Predicting the performance of amorphous and crystalline silicon based photovoltaic solar thermal collectors. Energy Conversion and Management, 52(3), 1741–1747. https://doi.org/10.1016/j.enconman.2010.10.039DaghighR.IbrahimA.JinG.L.RuslanM.H.SopianK.2011Predicting the performance of amorphous and crystalline silicon based photovoltaic solar thermal collectorsEnergy Conversion and Management52317411747https://doi.org/10.1016/j.enconman.2010.10.03910.1016/j.enconman.2010.10.039Search in Google Scholar

De Solla Price, D., & Gürsey, S. (1975). Studies in Scientometrics I Transience and Continuance in Scientific Authorship. Ciência Da Informação, 4(1).De Solla PriceD.GürseyS.1975Studies in Scientometrics I Transience and Continuance in Scientific AuthorshipCiência Da Informação41Search in Google Scholar

Dominković, D.F., Bačeković, I., Ćosić, B., Krajačić, G., Pukšec, T., Duić, N., & Markovska, N. (2016). Zero carbon energy system of South East Europe in 2050. Applied Energy, 184, 1517–1528. https://doi.org/10.1016/j.apenergy.2016.03.046DominkovićD.F.BačekovićI.ĆosićB.KrajačićG.PukšecT.DuićN.MarkovskaN.2016Zero carbon energy system of South East Europe in 2050Applied Energy18415171528https://doi.org/10.1016/j.apenergy.2016.03.04610.1016/j.apenergy.2016.03.046Search in Google Scholar

Du, E., Zhang, N., Hodge, B.M., Kang, C., Kroposki, B., & Xia, Q. (2018). Economic justification of concentrating solar power in high renewable energy penetrated power systems. Applied Energy, 222, 649–661. https://doi.org/10.1016/j.apenergy.2018.03.161DuE.ZhangN.HodgeB.M.KangC.KroposkiB.XiaQ.2018Economic justification of concentrating solar power in high renewable energy penetrated power systemsApplied Energy222649661https://doi.org/10.1016/j.apenergy.2018.03.16110.1016/j.apenergy.2018.03.161Search in Google Scholar

Du, E., Zhang, N., Hodge, B.M., Wang, Q., Lu, Z., Kang, C., Kroposki, B., & Xia, Q. (2019). Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment model. IEEE Transactions on Power Systems, 34(1), 140–151. https://doi.org/10.1109/TPWRS.2018.2866486DuE.ZhangN.HodgeB.M.WangQ.LuZ.KangC.KroposkiB.XiaQ.2019Operation of a high renewable penetrated power system with CSP plants: A look-ahead stochastic unit commitment modelIEEE Transactions on Power Systems341140151https://doi.org/10.1109/TPWRS.2018.286648610.1109/TPWRS.2018.2866486Search in Google Scholar

Ducom, G., Gautier, M., Pietraccini, M., Tagutchou, J.P., Lebouil, D., & Gourdon, R. (2020). Comparative analyses of three olive mill solid residues from different countries and processes for energy recovery by gasification. Renewable Energy, 145, 180–189. https://doi.org/10.1016/j.renene.2019.05.116DucomG.GautierM.PietracciniM.TagutchouJ.P.LebouilD.GourdonR.2020Comparative analyses of three olive mill solid residues from different countries and processes for energy recovery by gasificationRenewable Energy145180189https://doi.org/10.1016/j.renene.2019.05.11610.1016/j.renene.2019.05.116Search in Google Scholar

Fagiano, L., & Schnez, S. (2017). On the take-off of airborne wind energy systems based on rigid wings. Renewable Energy, 107, 473–488. https://doi.org/10.1016/j.renene.2017.02.023FagianoL.SchnezS.2017On the take-off of airborne wind energy systems based on rigid wingsRenewable Energy107473488https://doi.org/10.1016/j.renene.2017.02.02310.1016/j.renene.2017.02.023Search in Google Scholar

Fagiano, Lorenzo, Milanese, M., & Piga, D. (2010). High-altitude wind power generation. IEEE Transactions on Energy Conversion, 25(1), 168–180. https://doi.org/10.1109/TEC.2009.2032582FagianoLorenzoMilaneseM.PigaD.2010High-altitude wind power generationIEEE Transactions on Energy Conversion251168180https://doi.org/10.1109/TEC.2009.203258210.1109/TEC.2009.2032582Search in Google Scholar

Fayaz, H., Rahim, N.A., Hasanuzzaman, M., Nasrin, R., & Rivai, A. (2019). Numerical and experimental investigation of the effect of operating conditions on performance of PVT and PVT-PCM. Renewable Energy, 143, 827–841. https://doi.org/10.1016/j.renene.2019.05.041FayazH.RahimN.A.HasanuzzamanM.NasrinR.RivaiA.2019Numerical and experimental investigation of the effect of operating conditions on performance of PVT and PVT-PCMRenewable Energy143827841https://doi.org/10.1016/j.renene.2019.05.04110.1016/j.renene.2019.05.041Search in Google Scholar

Garfield, E. (1994). Scientography: Mapping the tracks of science. Contents: Social & Behavioral Sciences, 7(45), 5–10.GarfieldE.1994Scientography: Mapping the tracks of scienceContents: Social & Behavioral Sciences745510Search in Google Scholar

Garner, J., Porter, A.L., Leidolf, A., & Baker, M. (2020). Measuring and visualizing research collaboration and productivity. Journal of Data and Information Science, 3(1), 54–81. https://doi.org/10.2478/jdis-2018-0004GarnerJ.PorterA.L.LeidolfA.BakerM.2020Measuring and visualizing research collaboration and productivityJournal of Data and Information Science315481https://doi.org/10.2478/jdis-2018-000410.2478/jdis-2018-0004Search in Google Scholar

Gibb, D., Johnson, M., Romaní, J., Gasia, J., Cabeza, L.F., & Seitz, A. (2018). Process integration of thermal energy storage systems – Evaluation methodology and case studies. Applied Energy, 230, 750–760. https://doi.org/10.1016/j.apenergy.2018.09.001GibbD.JohnsonM.RomaníJ.GasiaJ.CabezaL.F.SeitzA.2018Process integration of thermal energy storage systems – Evaluation methodology and case studiesApplied Energy230750760https://doi.org/10.1016/j.apenergy.2018.09.00110.1016/j.apenergy.2018.09.001Search in Google Scholar

Granovskii, M., Dincer, I., & Rosen, M.A. (2007). Exergetic life cycle assessment of hydrogen production from renewables. Journal of Power Sources, 167(2), 461–471. https://doi.org/10.1016/j.jpowsour.2007.02.031GranovskiiM.DincerI.RosenM.A.2007Exergetic life cycle assessment of hydrogen production from renewablesJournal of Power Sources1672461471https://doi.org/10.1016/j.jpowsour.2007.02.03110.1016/j.jpowsour.2007.02.031Search in Google Scholar

Guler, A.T., Waaijer, C.J.F., Mohammed, Y., & Palmblad, M. (2016). Automating bibliometric analyses using Taverna scientific workflows: A tutorial on integrating Web Services. Journal of Informetrics, 10(3), 830–841. https://doi.org/10.1016/j.joi.2016.05.002GulerA.T.WaaijerC.J.F.MohammedY.PalmbladM.2016Automating bibliometric analyses using Taverna scientific workflows: A tutorial on integrating Web ServicesJournal of Informetrics103830841https://doi.org/10.1016/j.joi.2016.05.00210.1016/j.joi.2016.05.002Search in Google Scholar

Hacatoglu, K., Dincer, I., & Rosen, M.A. (2011). Exergy analysis of a hybrid solar hydrogen system with activated carbon storage. International Journal of Hydrogen Energy, 36(5), 3273–3282. https://doi.org/10.1016/j.ijhydene.2010.12.034HacatogluK.DincerI.RosenM.A.2011Exergy analysis of a hybrid solar hydrogen system with activated carbon storageInternational Journal of Hydrogen Energy36532733282https://doi.org/10.1016/j.ijhydene.2010.12.03410.1016/j.ijhydene.2010.12.034Search in Google Scholar

Hajibandeh, N., Shafie-khah, M., Osório, G.J., Aghaei, J., & Catalão, J.P.S. (2018). A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators. Applied Energy, 212, 721–732. https://doi.org/10.1016/j.apenergy.2017.12.076HajibandehN.Shafie-khahM.OsórioG.J.AghaeiJ.CatalãoJ.P.S.2018A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generatorsApplied Energy212721732https://doi.org/10.1016/j.apenergy.2017.12.07610.1016/j.apenergy.2017.12.076Search in Google Scholar

Hanel, M., & Escobar, R. (2013). Influence of solar energy resource assessment uncertainty in the levelized electricity cost of concentrated solar power plants in Chile. Renewable Energy, 49, 96–100. https://doi.org/10.1016/j.renene.2012.01.056HanelM.EscobarR.2013Influence of solar energy resource assessment uncertainty in the levelized electricity cost of concentrated solar power plants in ChileRenewable Energy4996100https://doi.org/10.1016/j.renene.2012.01.05610.1016/j.renene.2012.01.056Search in Google Scholar

Haseeb, M., Abidin, I.S.Z., Hye, Q.M.A., & Hartani, N.H. (2019). The impact of renewable energy on economic well-being of Malaysia: Fresh evidence from auto regressive distributed lag bound testing approach. International Journal of Energy Economics and Policy, 9(1), 269–275. https://doi.org/10.32479/ijeep.7229HaseebM.AbidinI.S.Z.HyeQ.M.A.HartaniN.H.2019The impact of renewable energy on economic well-being of Malaysia: Fresh evidence from auto regressive distributed lag bound testing approachInternational Journal of Energy Economics and Policy91269275https://doi.org/10.32479/ijeep.722910.32479/ijeep.7229Search in Google Scholar

Hassan, A., Wahab, A., Qasim, M.A., Janjua, M.M., Ali, M.A., Ali, H.M., Jadoon, T.R., Ali, E., Raza, A., & Javaid, N. (2020). Thermal management and uniform temperature regulation of photovoltaic modules using hybrid phase change materials-nanofluids system. Renewable Energy, 145, 282–293. https://doi.org/10.1016/j.renene.2019.05.130HassanA.WahabA.QasimM.A.JanjuaM.M.AliM.A.AliH.M.JadoonT.R.AliE.RazaA.JavaidN.2020Thermal management and uniform temperature regulation of photovoltaic modules using hybrid phase change materials-nanofluids systemRenewable Energy145282293https://doi.org/10.1016/j.renene.2019.05.13010.1016/j.renene.2019.05.130Search in Google Scholar

Hodge, B.M., Brancucci Martinez-Anido, C., Wang, Q., Chartan, E., Florita, A., & Kiviluoma, J. (2018). The combined value of wind and solar power forecasting improvements and electricity storage. Applied Energy, 214, 1–15. https://doi.org/10.1016/j.apenergy.2017.12.120HodgeB.M.Brancucci Martinez-AnidoC.WangQ.ChartanE.FloritaA.KiviluomaJ.2018The combined value of wind and solar power forecasting improvements and electricity storageApplied Energy214115https://doi.org/10.1016/j.apenergy.2017.12.12010.1016/j.apenergy.2017.12.120Search in Google Scholar

Hu, C., Chen, X., Dai, Q., Wang, M., Qu, L., & Dai, L. (2017). Earth-abundant carbon catalysts for renewable generation of clean energy from sunlight and water. Nano Energy, 41, 367–376. https://doi.org/10.1016/j.nanoen.2017.09.029HuC.ChenX.DaiQ.WangM.QuL.DaiL.2017Earth-abundant carbon catalysts for renewable generation of clean energy from sunlight and waterNano Energy41367376https://doi.org/10.1016/j.nanoen.2017.09.02910.1016/j.nanoen.2017.09.029Search in Google Scholar

IRENA. (2018). Renewable Energy and Jobs – Annual Review 2018. In /publications/2018/May/Renewable-Energy-and-Jobs-Annual-Review-2018. https://www.irena.org/publications/2018/May/Renewable-Energy-and-Jobs-Annual-Review-2018IRENA2018Renewable Energy and Jobs – Annual Review 2018In /publications/2018/May/Renewable-Energy-and-Jobs-Annual-Review-2018. https://www.irena.org/publications/2018/May/Renewable-Energy-and-Jobs-Annual-Review-2018Search in Google Scholar

Jacob, R., Belusko, M., Inés Fernández, A., Cabeza, L.F., Saman, W., & Bruno, F. (2016). Embodied energy and cost of high temperature thermal energy storage systems for use with concentrated solar power plants. Applied Energy, 180, 586–597. https://doi.org/10.1016/j.apenergy.2016.08.027JacobR.BeluskoM.Inés FernándezA.CabezaL.F.SamanW.BrunoF.2016Embodied energy and cost of high temperature thermal energy storage systems for use with concentrated solar power plantsApplied Energy180586597https://doi.org/10.1016/j.apenergy.2016.08.02710.1016/j.apenergy.2016.08.027Search in Google Scholar

Khalid, F., Dincer, I., & Rosen, M.A. (2015). Energy and exergy analyses of a solar-biomass integrated cycle for multigeneration. Solar Energy, 112, 290–299. https://doi.org/10.1016/j.solener.2014.11.027KhalidF.DincerI.RosenM.A.2015Energy and exergy analyses of a solar-biomass integrated cycle for multigenerationSolar Energy112290299https://doi.org/10.1016/j.solener.2014.11.02710.1016/j.solener.2014.11.027Search in Google Scholar

Kipper, L.M., Furstenau, L.B., Hoppe, D., Frozza, R., & Iepsen, S. (2020). Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis. International Journal of Production Research, 58(6), 1605–1627. https://doi.org/10.1080/00207543.2019.1671625KipperL.M.FurstenauL.B.HoppeD.FrozzaR.IepsenS.2020Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysisInternational Journal of Production Research58616051627https://doi.org/10.1080/00207543.2019.167162510.1080/00207543.2019.1671625Search in Google Scholar

Komendantova, N., Patt, A., Barras, L., & Battaglini, A. (2012). Perception of risks in renewable energy projects: The case of concentrated solar power in North Africa. Energy Policy, 40(1), 103–109. https://doi.org/10.1016/j.enpol.2009.12.008KomendantovaN.PattA.BarrasL.BattagliniA.2012Perception of risks in renewable energy projects: The case of concentrated solar power in North AfricaEnergy Policy401103109https://doi.org/10.1016/j.enpol.2009.12.00810.1016/j.enpol.2009.12.008Search in Google Scholar

Krajačić, G., Vujanović, M., Duić, N., Kılkış, Ş., Rosen, M.A., & Ahmad Al-Nimr, M. (2018). Integrated approach for sustainable development of energy, water and environment systems. Energy Conversion and Management, 159, 398–412. https://doi.org/10.1016/j.enconman.2017.12.016KrajačićG.VujanovićM.DuićN.KılkışŞ.RosenM.A.Ahmad Al-NimrM.2018Integrated approach for sustainable development of energy, water and environment systemsEnergy Conversion and Management159398412https://doi.org/10.1016/j.enconman.2017.12.01610.1016/j.enconman.2017.12.016Search in Google Scholar

Kumar, R.S., & Kaliyaperumal, K. (2015). A scientometric analysis of mobile technology publications. Scientometrics, 105, 921–939. https://doi.org/10.1007/s11192-015-1710-7KumarR.S.KaliyaperumalK.2015A scientometric analysis of mobile technology publicationsScientometrics105921939https://doi.org/10.1007/s11192-015-1710-710.1007/s11192-015-1710-7Search in Google Scholar

Leblanc, J., Andrews, J., & Akbarzadeh, A. (2010). Low-temperature solar-thermal multi-effect evaporation desalination systems. International Journal of Energy Research, 34(5), 393–403. https://doi.org/10.1002/er.1642LeblancJ.AndrewsJ.AkbarzadehA.2010Low-temperature solar-thermal multi-effect evaporation desalination systemsInternational Journal of Energy Research345393403https://doi.org/10.1002/er.164210.1002/er.1642Search in Google Scholar

Letcher, T.M. (2018). Why Solar Energy? In A Comprehensive Guide to Solar Energy Systems (pp. 3–16). Elsevier. https://doi.org/10.1016/b978-0-12-811479-7.00001-4LetcherT.M.2018Why Solar Energy?InA Comprehensive Guide to Solar Energy Systems316Elsevierhttps://doi.org/10.1016/b978-0-12-811479-7.00001-410.1016/B978-0-12-811479-7.00001-4Search in Google Scholar

Leydesdorff, L., & Persson, O. (2010). Mapping the geography of science: Distribution patterns and networks of relations among cities and institutes. Journal of the American Society for Information Science and Technology, 61(8), 1622–1634. https://doi.org/10.1002/asi.21347LeydesdorffL.PerssonO.2010Mapping the geography of science: Distribution patterns and networks of relations among cities and institutesJournal of the American Society for Information Science and Technology61816221634https://doi.org/10.1002/asi.2134710.1002/asi.21347Search in Google Scholar

Light, R.P., Polley, D.E., & Börner, K. (2014). Open data and open code for big science of science studies. Scientometrics, 101, 1535–1551. https://doi.org/10.1007/s11192-014-1238-2LightR.P.PolleyD.E.BörnerK.2014Open data and open code for big science of science studiesScientometrics10115351551https://doi.org/10.1007/s11192-014-1238-210.1007/s11192-014-1238-2Search in Google Scholar

Liu, X., Feng, X., & He, Y. (2019). Rapid discrimination of the categories of the biomass pellets using laser-induced breakdown spectroscopy. Renewable Energy, 143, 176–182. https://doi.org/10.1016/j.renene.2019.04.137LiuX.FengX.HeY.2019Rapid discrimination of the categories of the biomass pellets using laser-induced breakdown spectroscopyRenewable Energy143176182https://doi.org/10.1016/j.renene.2019.04.13710.1016/j.renene.2019.04.137Search in Google Scholar

Longo, M., Foiadelli, F., & Yaïci, W. (2019). Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district. International Journal of Energy and Environmental Engineering, 10(3), 271–285. https://doi.org/10.1007/s40095-019-0301-4LongoM.FoiadelliF.YaïciW.2019Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential districtInternational Journal of Energy and Environmental Engineering103271285https://doi.org/10.1007/s40095-019-0301-410.1007/s40095-019-0301-4Search in Google Scholar

Lopez-Rey, A., Campinez-Romero, S., Gil-Ortego, R., & Colmenar-Santos, A. (2019). Evaluation of supply–demand adaptation of photovoltaic–wind hybrid plants integrated into an urban environment. Energies, 12(9), 1780. https://doi.org/10.3390/en12091780Lopez-ReyA.Campinez-RomeroS.Gil-OrtegoR.Colmenar-SantosA.2019Evaluation of supply–demand adaptation of photovoltaic–wind hybrid plants integrated into an urban environmentEnergies1291780https://doi.org/10.3390/en1209178010.3390/en12091780Search in Google Scholar

Madrazo, A., González, A., Martínez, R., Domingo, R., Mañana, M., Arroyo, A., Castro, P.B., Silió, D., & Lecuna, R. (2015). Analysis of a real case of ampacity management in a 132 kV network integrating high rates of wind energy. Renewable Energy and Power Quality Journal, 1(13), 797–800. https://doi.org/10.24084/repqj13.513MadrazoA.GonzálezA.MartínezR.DomingoR.MañanaM.ArroyoA.CastroP.B.SilióD.LecunaR.2015Analysis of a real case of ampacity management in a 132 kV network integrating high rates of wind energyRenewable Energy and Power Quality Journal113797800https://doi.org/10.24084/repqj13.51310.24084/repqj13.513Search in Google Scholar

Madrazo, A., González, A., Martínez, R., Mañana, M., Hervás, E., Arroyo, A., Castro, P.B., & Silió, D. (2013). Increasing grid integration of wind energy by using ampacity techniques. Renewable Energy and Power Quality Journal, 1(11), 1121–1124. https://doi.org/10.24084/repqj11.549MadrazoA.GonzálezA.MartínezR.MañanaM.HervásE.ArroyoA.CastroP.B.SilióD.2013Increasing grid integration of wind energy by using ampacity techniquesRenewable Energy and Power Quality Journal11111211124https://doi.org/10.24084/repqj11.54910.24084/repqj11.549Search in Google Scholar

Maleki, A., Rosen, M.A., & Pourfayaz, F. (2017). Optimal operation of a grid-connected hybrid renewable energy system for residential applications. Sustainability (Switzerland), 9(8), 1314. https://doi.org/10.3390/su9081314MalekiA.RosenM.A.PourfayazF.2017Optimal operation of a grid-connected hybrid renewable energy system for residential applicationsSustainability (Switzerland)981314https://doi.org/10.3390/su908131410.3390/su9081314Search in Google Scholar

Martí-Ballester, C.P. (2019). Do European renewable energy mutual funds foster the transition to a low-carbon economy? Renewable Energy, 143, 1299–1309. https://doi.org/10.1016/j.renene.2019.05.095Martí-BallesterC.P.2019Do European renewable energy mutual funds foster the transition to a low-carbon economy?Renewable Energy14312991309https://doi.org/10.1016/j.renene.2019.05.09510.1016/j.renene.2019.05.095Search in Google Scholar

Martínez, M.A., Cobo, M.J., Herrera, M., & Herrera-Viedma, E. (2015). Analyzing the Scientific Evolution of Social Work Using Science Mapping. Research on Social Work Practice, 25(2), 257–277. https://doi.org/10.1177/1049731514522101MartínezM.A.CoboM.J.HerreraM.Herrera-ViedmaE.2015Analyzing the Scientific Evolution of Social Work Using Science MappingResearch on Social Work Practice252257277https://doi.org/10.1177/104973151452210110.1177/1049731514522101Search in Google Scholar

Mena, R., Escobar, R., Lorca, Negrete-Pincetic, M., & Olivares, D. (2019). The impact of concentrated solar power in electric power systems: A Chilean case study. Applied Energy, 235, 258–283. https://doi.org/10.1016/j.apenergy.2018.10.088MenaR.EscobarR.LorcaNegrete-PinceticM.OlivaresD.2019The impact of concentrated solar power in electric power systems: A Chilean case studyApplied Energy235258283https://doi.org/10.1016/j.apenergy.2018.10.08810.1016/j.apenergy.2018.10.088Search in Google Scholar

Moeller, C., Meiss, J., Mueller, B., Hlusiak, M., Breyer, C., Kastner, M., & Twele, J. (2014). Transforming the electricity generation of the Berlin-Brandenburg region, Germany. Renewable Energy, 72, 39–50. https://doi.org/10.1016/j.renene.2014.06.042MoellerC.MeissJ.MuellerB.HlusiakM.BreyerC.KastnerM.TweleJ.2014Transforming the electricity generation of the Berlin-Brandenburg region, GermanyRenewable Energy723950https://doi.org/10.1016/j.renene.2014.06.04210.1016/j.renene.2014.06.042Search in Google Scholar

Nara, E.O.B., Schaefer, J.L., de Moraes, J., Tedesco, L.P.C., Furtado, J.C., & Baierle, I.C. (2019). Sourcing research papers on small- and medium-sized enterprises’ competitiveness: An approach based on authors’ networks. Revista Espanola de Documentacion Cientifica, 42(2), e230. https://doi.org/10.3989/redc.2019.2.1602NaraE.O.B.SchaeferJ.L.de MoraesJ.TedescoL.P.C.FurtadoJ.C.BaierleI.C.2019Sourcing research papers on small- and medium-sized enterprises’ competitiveness: An approach based on authors’ networksRevista Espanola de Documentacion Cientifica422e230https://doi.org/10.3989/redc.2019.2.160210.3989/redc.2019.2.1602Search in Google Scholar

Nazri, N.S., Fudholi, A., Ruslan, M.H., & Sopian, K. (2018). Mathematical Modeling of Photovoltaic Thermal-Thermoelectric (PVT-TE) Air Collector. International Journal of Power Electronics and Drive System (IJPEDS), 9(2), 795–802. https://doi.org/10.11591/ijpeds.v9.i2.pp795-802NazriN.S.FudholiA.RuslanM.H.SopianK.2018Mathematical Modeling of Photovoltaic Thermal-Thermoelectric (PVT-TE) Air CollectorInternational Journal of Power Electronics and Drive System (IJPEDS)92795802https://doi.org/10.11591/ijpeds.v9.i2.pp795-80210.11591/ijpeds.v9.i2.pp795-802Search in Google Scholar

Newman, M.E.J. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E – Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 64(1), 016131. https://doi.org/10.1103/PhysRevE.64.016131NewmanM.E.J.2001aScientific collaboration networks. I. Network construction and fundamental resultsPhysical Review E – Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics641016131. https://doi.org/10.1103/PhysRevE.64.01613110.1103/PhysRevE.64.01613111461355Search in Google Scholar

Newman, M.E.J. (2001b). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E – Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 64(1), 016132. https://doi.org/10.1103/PhysRevE.64.016132NewmanM.E.J.2001bScientific collaboration networks. II. Shortest paths, weighted networks, and centralityPhysical Review E – Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics641016132. https://doi.org/10.1103/PhysRevE.64.01613210.1103/PhysRevE.64.01613211461356Search in Google Scholar

Orwig, K.D., Ahlstrom, M.L., Banunarayanan, V., Sharp, J., Wilczak, J.M., Freedman, J., Haupt, S.E., Cline, J., Bartholomy, O., Hamann, H.F., Hodge, B.M., Finley, C., Nakafuji, D., Peterson, J.L., Maggio, D., & Marquis, M. (2015). Recent trends in variable generation forecasting and its value to the power system. IEEE Transactions on Sustainable Energy, 6(3), 924–933. https://doi.org/10.1109/TSTE.2014.2366118OrwigK.D.AhlstromM.L.BanunarayananV.SharpJ.WilczakJ.M.FreedmanJ.HauptS.E.ClineJ.BartholomyO.HamannH.F.HodgeB.M.FinleyC.NakafujiD.PetersonJ.L.MaggioD.MarquisM.2015Recent trends in variable generation forecasting and its value to the power systemIEEE Transactions on Sustainable Energy63924933https://doi.org/10.1109/TSTE.2014.236611810.1109/TSTE.2014.2366118Search in Google Scholar

Osório, G.J., Lujano-Rojas, J.M., Matias, J.C.O., & Catalão, J.P.S. (2015). A new scenario generation-based method to solve the unit commitment problem with high penetration of renewable energies. International Journal of Electrical Power and Energy Systems, 64, 1063–1072. https://doi.org/10.1016/j.ijepes.2014.09.010OsórioG.J.Lujano-RojasJ.M.MatiasJ.C.O.CatalãoJ.P.S.2015A new scenario generation-based method to solve the unit commitment problem with high penetration of renewable energiesInternational Journal of Electrical Power and Energy Systems6410631072https://doi.org/10.1016/j.ijepes.2014.09.01010.1016/j.ijepes.2014.09.010Search in Google Scholar

Othman, M.Y., Ibrahim, A., Jin, G.L., Ruslan, M.H., & Sopian, K. (2013). Photovoltaic-thermal (PV/T) technology – The future energy technology. Renewable Energy, 49, 171–174. https://doi.org/10.1016/j.renene.2012.01.038OthmanM.Y.IbrahimA.JinG.L.RuslanM.H.SopianK.2013Photovoltaic-thermal (PV/T) technology – The future energy technologyRenewable Energy49171174https://doi.org/10.1016/j.renene.2012.01.03810.1016/j.renene.2012.01.038Search in Google Scholar

Oyedepo, S.O., Adaramola, M.S., & Paul, S.S. (2012). Analysis of wind speed data and wind energy potential in three selected locations in South-East Nigeria. International Journal of Energy and Environmental Engineering, 3(1), 1–11. https://doi.org/10.1186/2251-6832-3-7OyedepoS.O.AdaramolaM.S.PaulS.S.2012Analysis of wind speed data and wind energy potential in three selected locations in South-East NigeriaInternational Journal of Energy and Environmental Engineering31111https://doi.org/10.1186/2251-6832-3-710.1201/b18529-3Search in Google Scholar

Parliament, E. (2009). Directiva 2009/28/CE do Parlamento Europeu e do Conselho. Jornal Oficial Da União Europeia, 47. https://eur-lex.europa.eu/legal-content/PT/TXT/PDF/?uri=CELEX:32009L0028&from=ENParliamentE.2009Directiva 2009/28/CE do Parlamento Europeu e do ConselhoJornal Oficial Da União Europeia47https://eur-lex.europa.eu/legal-content/PT/TXT/PDF/?uri=CELEX:32009L0028&from=ENSearch in Google Scholar

Peiró, G., Prieto, C., Gasia, J., Jové, A., Miró, L., & Cabeza, L.F. (2018). Two-tank molten salts thermal energy storage system for solar power plants at pilot plant scale: Lessons learnt and recommendations for its design, start-up and operation. Renewable Energy, 121, 236–248. https://doi.org/10.1016/j.renene.2018.01.026PeiróG.PrietoC.GasiaJ.JovéA.MiróL.CabezaL.F.2018Two-tank molten salts thermal energy storage system for solar power plants at pilot plant scale: Lessons learnt and recommendations for its design, start-up and operationRenewable Energy121236248https://doi.org/10.1016/j.renene.2018.01.02610.1016/j.renene.2018.01.026Search in Google Scholar

Pfeifer, A., Krajačić, G., Ljubas, D., & Duić, N. (2019). Increasing the integration of solar photovoltaics in energy mix on the road to low emissions energy system – Economic and environmental implications. Renewable Energy, 143, 1310–1317. https://doi.org/10.1016/j.renene.2019.05.080PfeiferA.KrajačićG.LjubasD.DuićN.2019Increasing the integration of solar photovoltaics in energy mix on the road to low emissions energy system – Economic and environmental implicationsRenewable Energy14313101317https://doi.org/10.1016/j.renene.2019.05.08010.1016/j.renene.2019.05.080Search in Google Scholar

Poole, A.D., Barnett, A.M., Boes, E., Weinberg, C.J., Ogden, J.M., Carlson, D.E., ..., & Nitsch, J. (1993). Renewable Energy: Sources for fuels and electricity. Island Press. https://books.google.com.br/books?hl=pt-BR&lr=&id=40XtqVMRxOUC&oi=fnd&pg=PA1&dq=Grubb,+M.+J.,+%26+Meyer,+N.+I.+(1993).+Wind+resources.+Renewable+Energy:+Sources+for+Fuels+and+Electricity,+198.&ots=j0ItF__mPr&sig=DFzX4tTyS4dsxCY_iKyjDOnCRc8PooleA.D.BarnettA.M.BoesE.WeinbergC.J.OgdenJ.M.CarlsonD.E.NitschJ.1993Renewable Energy: Sources for fuels and electricityIsland Presshttps://books.google.com.br/books?hl=pt-BR&lr=&id=40XtqVMRxOUC&oi=fnd&pg=PA1&dq=Grubb,+M.+J.,+%26+Meyer,+N.+I.+(1993).+Wind+resources.+Renewable+Energy:+Sources+for+Fuels+and+Electricity,+198.&ots=j0ItF__mPr&sig=DFzX4tTyS4dsxCY_iKyjDOnCRc8Search in Google Scholar

Rasat, M.S.M., Wahab, R., Mohamed, M., Iqbal Ahmad, M., Hazim Mohamad Amini, M., Mohd Nazri Wan Abdul Rahman, W., Khairul Azhar Abdul Razab, M., Ahmad Mohd Yunus, A., Kelantan, M., & Campus, J. (2016). Preliminary study on properties of small diameter wild leucaena leucocephala species as potential biomass energy sources. ARPN Journal of Engineering and Applied Sciences, 11(9). www.arpnjournals.comRasatM.S.M.WahabR.MohamedM.Iqbal AhmadM.Hazim Mohamad AminiM.Mohd Nazri Wan Abdul RahmanW.Khairul Azhar Abdul RazabM.Ahmad Mohd YunusA.KelantanM.CampusJ.2016Preliminary study on properties of small diameter wild leucaena leucocephala species as potential biomass energy sourcesARPN Journal of Engineering and Applied Sciences119www.arpnjournals.comSearch in Google Scholar

Ren, C., An, N., Wang, J., Li, L., Hu, B., & Shang, D. (2014). Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226–239. https://doi.org/10.1016/j.knosys.2013.11.015RenC.AnN.WangJ.LiL.HuB.ShangD.2014Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecastingKnowledge-Based Systems56226239https://doi.org/10.1016/j.knosys.2013.11.01510.1016/j.knosys.2013.11.015Search in Google Scholar

Rezaie, B., Reddy, B.V., & Rosen, M.A. (2018). Exergy Assessment of a Solar-Assisted District Energy System. The Open Fuels & Energy Science Journal, 11, 30. https://doi.org/10.2174/1876973x01811010030RezaieB.ReddyB.V.RosenM.A.2018Exergy Assessment of a Solar-Assisted District Energy SystemThe Open Fuels & Energy Science Journal1130https://doi.org/10.2174/1876973x0181101003010.2174/1876973X01811010030Search in Google Scholar

Rodrigues, E.M.G., Osório, G.J., Godina, R., Bizuayehu, A.W., Lujano-Rojas, J.M., Matias, J.C.O., & Catalão, J.P.S. (2015). Modelling and sizing of NaS (sodium sulfur) battery energy storage system for extending wind power performance in Crete Island. Energy, 90 Part 2, 1606–1617. https://doi.org/10.1016/j.energy.2015.06.116RodriguesE.M.G.OsórioG.J.GodinaR.BizuayehuA.W.Lujano-RojasJ.M.MatiasJ.C.O.CatalãoJ.P.S.2015Modelling and sizing of NaS (sodium sulfur) battery energy storage system for extending wind power performance in Crete IslandEnergy90Part 216061617https://doi.org/10.1016/j.energy.2015.06.11610.1016/j.energy.2015.06.116Search in Google Scholar

Rosa, C.B., Rediske, G., Rigo, P.D., Wendt, J.F.M., Michels, L., & Siluk, J.C.M. (2018). Development of a computational tool for measuring organizational competitiveness in the photovoltaic power plants. Energies, 11(4). https://doi.org/10.3390/en11040867RosaC.B.RediskeG.RigoP.D.WendtJ.F.M.MichelsL.SilukJ.C.M.2018Development of a computational tool for measuring organizational competitiveness in the photovoltaic power plantsEnergies114https://doi.org/10.3390/en1104086710.3390/en11040867Search in Google Scholar

Roselli, C., Diglio, G., Sasso, M., & Tariello, F. (2019). A novel energy index to assess the impact of a solar PV-based ground source heat pump on the power grid. Renewable Energy, 143, 488–500. https://doi.org/10.1016/j.renene.2019.05.023RoselliC.DiglioG.SassoM.TarielloF.2019A novel energy index to assess the impact of a solar PV-based ground source heat pump on the power gridRenewable Energy143488500https://doi.org/10.1016/j.renene.2019.05.02310.1016/j.renene.2019.05.023Search in Google Scholar

Ruiz-Cabañas, F.J., Prieto, C., Madina, V., Fernández, A.I., & Cabeza, L.F. (2017). Materials selection for thermal energy storage systems in parabolic trough collector solar facilities using high chloride content nitrate salts. Solar Energy Materials and Solar Cells, 163, 134–147. https://doi.org/10.1016/j.solmat.2017.01.028Ruiz-CabañasF.J.PrietoC.MadinaV.FernándezA.I.CabezaL.F.2017Materials selection for thermal energy storage systems in parabolic trough collector solar facilities using high chloride content nitrate saltsSolar Energy Materials and Solar Cells163134147https://doi.org/10.1016/j.solmat.2017.01.02810.1016/j.solmat.2017.01.028Search in Google Scholar

Rukman, N.S.B., Fudholi, A., Taslim, I., Indrianti, M.A., Manyoe, I.N., Lestari, U., & Sopian, K. (2019). Energy and exergy efficiency of water-based photovoltaic thermal (PVT) systems: An overview. International Journal of Power Electronics and Drive Systems, 10(2), 987–994. https://doi.org/10.11591/ijpeds.v10.i2.pp987-994RukmanN.S.B.FudholiA.TaslimI.IndriantiM.A.ManyoeI.N.LestariU.SopianK.2019Energy and exergy efficiency of water-based photovoltaic thermal (PVT) systems: An overviewInternational Journal of Power Electronics and Drive Systems102987994https://doi.org/10.11591/ijpeds.v10.i2.pp987-99410.11591/ijpeds.v10.i2.pp987-994Search in Google Scholar

Sakamoto, R., Senjyu, T., Kaneko, T., Urasaki, N., Takagi, T., & Sugimoto, S. (2008). Output power leveling of wind turbine generator by pitch angle control using H⧜ control. Electrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi), 162(4), 17–24. https://doi.org/10.1002/eej.20657SakamotoR.SenjyuT.KanekoT.UrasakiN.TakagiT.SugimotoS.2008Output power leveling of wind turbine generator by pitch angle control using H⧜ controlElectrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi)16241724https://doi.org/10.1002/eej.2065710.1109/PSCE.2006.296239Search in Google Scholar

Salameh, Z., New York, L., & Diego, S. (2014). Renewable Energy System Design. Academic Press. http://elsevier.com/SalamehZ.New YorkL.DiegoS.2014Renewable Energy System DesignAcademic Presshttp://elsevier.com/Search in Google Scholar

Sassmannshausen, S.P., & Volkmann, C. (2018). The Scientometrics of Social Entrepreneurship and Its Establishment as an Academic Field. Journal of Small Business Management, 56(2), 251–273. https://doi.org/10.1111/jsbm.12254SassmannshausenS.P.VolkmannC.2018The Scientometrics of Social Entrepreneurship and Its Establishment as an Academic FieldJournal of Small Business Management562251273https://doi.org/10.1111/jsbm.1225410.1111/jsbm.12254Search in Google Scholar

Schaefer, J.L., Siluk, J.C.M., Carvalho, P.S. de, Renes Pinheiro, J., & Schneider, P.S. (2020). Management Challenges and Opportunities for Energy Cloud Development and Diffusion. Energies, 13(16), 4048. https://doi.org/10.3390/en13164048SchaeferJ.L.SilukJ.C.M.CarvalhoP.S. deRenes PinheiroJ.SchneiderP.S.2020Management Challenges and Opportunities for Energy Cloud Development and DiffusionEnergies13164048https://doi.org/10.3390/en1316404810.3390/en13164048Search in Google Scholar

Sci2 Tool. (2019). A Tool for Science of Science Research and Practice. https://sci2.cns.iu.edu/user/index.phpSci2 Tool2019A Tool for Science of Science Research and Practicehttps://sci2.cns.iu.edu/user/index.phpSearch in Google Scholar

Senjyu, T., Sakamoto, R., Urasaki, N., Higa, H., Uezato, K., & Funabashi, T. (2006). Output power control of wind turbine generator by pitch angle control using minimum variance control. Electrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi), 154(2), 10–18. https://doi.org/10.1002/eej.20247SenjyuT.SakamotoR.UrasakiN.HigaH.UezatoK.FunabashiT.2006Output power control of wind turbine generator by pitch angle control using minimum variance controlElectrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi)15421018https://doi.org/10.1002/eej.2024710.1002/eej.20247Search in Google Scholar

Shahbaz, M., Solarin, S.A., Hammoudeh, S., & Shahzad, S.J.H. (2017). Bounds testing approach to analyzing the environment Kuznets curve hypothesis with structural beaks: The role of biomass energy consumption in the United States. Energy Economics, 68, 548–565. https://doi.org/10.1016/j.eneco.2017.10.004ShahbazM.SolarinS.A.HammoudehS.ShahzadS.J.H.2017Bounds testing approach to analyzing the environment Kuznets curve hypothesis with structural beaks: The role of biomass energy consumption in the United StatesEnergy Economics68548565https://doi.org/10.1016/j.eneco.2017.10.00410.1016/j.eneco.2017.10.004Search in Google Scholar

Sharizal Sirrajudin, M., Sukhairi Mat Rasat, M., Wahab, R., Hazim Mohamad Amini, M., Mohamed, M., Iqbal Ahmad, M., Moktar, J., Azhar Ibrahim, M., Kelantan, M., & Campus, J. (2016). Enhancing the Energy Properties of Fugel Pellets from Oil Palm Fronds of Agricultural Residues by Mixing with Glycerin. 11(9). www.arpnjournals.comSharizal SirrajudinM.Sukhairi Mat RasatM.WahabR.Hazim Mohamad AminiM.MohamedM.Iqbal AhmadM.MoktarJ.Azhar IbrahimM.KelantanM.CampusJ.2016Enhancing the Energy Properties of Fugel Pellets from Oil Palm Fronds of Agricultural Residues by Mixing with Glycerin119www.arpnjournals.comSearch in Google Scholar

Singh, B., Baharin, N.A., Remeli, M.F., Oberoi, A., Date, A., & Akbarzadeh, A. (2017). Experimental Analysis of Thermoelectric Heat Exchanger for Power Generation from Salinity Gradient Solar Pond Using Low-Grade Heat. Journal of Electronic Materials, 46, 2854–2859. https://doi.org/10.1007/s11664-016-5009-0SinghB.BaharinN.A.RemeliM.F.OberoiA.DateA.AkbarzadehA.2017Experimental Analysis of Thermoelectric Heat Exchanger for Power Generation from Salinity Gradient Solar Pond Using Low-Grade HeatJournal of Electronic Materials4628542859https://doi.org/10.1007/s11664-016-5009-010.1007/s11664-016-5009-0Search in Google Scholar

Singh, R., Tundee, S., & Akbarzadeh, A. (2011). Electric power generation from solar pond using combined thermosyphon and thermoelectric modules. Solar Energy, 85(2), 371–378. https://doi.org/10.1016/j.solener.2010.11.012SinghR.TundeeS.AkbarzadehA.2011Electric power generation from solar pond using combined thermosyphon and thermoelectric modulesSolar Energy852371378https://doi.org/10.1016/j.solener.2010.11.01210.1016/j.solener.2010.11.012Search in Google Scholar

Sinha, A., Shahbaz, M., & Balsalobre, D. (2017). Exploring the relationship between energy usage segregation and environmental degradation in N-11 countries. Journal of Cleaner Production, 168, 1217–1229. https://doi.org/10.1016/j.jclepro.2017.09.071SinhaA.ShahbazM.BalsalobreD.2017Exploring the relationship between energy usage segregation and environmental degradation in N-11 countriesJournal of Cleaner Production16812171229https://doi.org/10.1016/j.jclepro.2017.09.07110.1016/j.jclepro.2017.09.071Search in Google Scholar

Skillicorn, D. (2007). Understanding complex datasets: Data mining with matrix decompositions. In Understanding Complex Datasets: Data Mining with Matrix Decompositions (1st Editio). https://doi.org/10.1201/9781584888338SkillicornD.2007Understanding complex datasets: Data mining with matrix decompositionsInUnderstanding Complex Datasets: Data Mining with Matrix Decompositions1st Editiohttps://doi.org/10.1201/978158488833810.1201/9781584888338Search in Google Scholar

Small, H., & Garfield, E. (1985). The geography of science: Disciplinary and national mappings. Journal of Information Science, 11(4), 147–159. https://doi.org/10.1177/016555158501100402SmallH.GarfieldE.1985The geography of science: Disciplinary and national mappingsJournal of Information Science114147159https://doi.org/10.1177/01655515850110040210.1177/016555158501100402Search in Google Scholar

Soltani, R., Mohammadzadeh Keleshtery, P., Vahdati, M., Khoshgoftarmanesh, M.H., Rosen, M.A., & Amidpour, M. (2014). Multi-objective optimization of a solar-hybrid cogeneration cycle: Application to CGAM problem. Energy Conversion and Management, 81, 60–71. https://doi.org/10.1016/j.enconman.2014.02.013SoltaniR.Mohammadzadeh KeleshteryP.VahdatiM.KhoshgoftarmaneshM.H.RosenM.A.AmidpourM.2014Multi-objective optimization of a solar-hybrid cogeneration cycle: Application to CGAM problemEnergy Conversion and Management816071https://doi.org/10.1016/j.enconman.2014.02.01310.1016/j.enconman.2014.02.013Search in Google Scholar

Stolarski, M.J., Szczukowski, S., Tworkowski, J., Krzyzaniak, M., Gulczyński, P., & Mleczek, M. (2013). Comparison of quality and production cost of briquettes made from agricultural and forest origin biomass. Renewable Energy, 57, 20–26. https://doi.org/10.1016/j.renene.2013.01.005StolarskiM.J.SzczukowskiS.TworkowskiJ.KrzyzaniakM.GulczyńskiP.MleczekM.2013Comparison of quality and production cost of briquettes made from agricultural and forest origin biomassRenewable Energy572026https://doi.org/10.1016/j.renene.2013.01.00510.1016/j.renene.2013.01.005Search in Google Scholar

Suarez, J.A., & Luengo, C.A. (2003). Coffee Husk Briquettes: A New Renewable Energy Source. Energy Sources, 25(10), 961–967. https://doi.org/10.1080/00908310303395SuarezJ.A.LuengoC.A.2003Coffee Husk Briquettes: A New Renewable Energy SourceEnergy Sources2510961967https://doi.org/10.1080/0090831030339510.1080/00908310390232415Search in Google Scholar

Tarfaoui, M., Nachtane, M., & Boudounit, H. (2019). Finite Element Analysis of Composite Offshore Wind Turbine Blades Under Operating Conditions. Journal of Thermal Science and Engineering Applications, 12(1), 011001. https://doi.org/10.1115/1.4042123TarfaouiM.NachtaneM.BoudounitH.2019Finite Element Analysis of Composite Offshore Wind Turbine Blades Under Operating ConditionsJournal of Thermal Science and Engineering Applications121011001. https://doi.org/10.1115/1.404212310.1115/1.4042123Search in Google Scholar

Tokimatsu, K., Konishi, S., Ishihara, K., Tezuka, T., Yasuoka, R., & Nishio, M. (2016). Role of innovative technologies under the global zero emissions scenarios. Applied Energy, 162, 1483–1493. https://doi.org/10.1016/j.apenergy.2015.02.051TokimatsuK.KonishiS.IshiharaK.TezukaT.YasuokaR.NishioM.2016Role of innovative technologies under the global zero emissions scenariosApplied Energy16214831493https://doi.org/10.1016/j.apenergy.2015.02.05110.1016/j.apenergy.2015.02.051Search in Google Scholar

Valderrama, C., Gibert, O., Arcal, J., Solano, P., Akbarzadeh, A., Larrotcha, E., & Cortina, J.L. (2011). Solar energy storage by salinity gradient solar pond: Pilot plant construction and gradient control. Desalination, 279(1–3), 445–450. https://doi.org/10.1016/j.desal.2011.06.035ValderramaC.GibertO.ArcalJ.SolanoP.AkbarzadehA.LarrotchaE.CortinaJ.L.2011Solar energy storage by salinity gradient solar pond: Pilot plant construction and gradient controlDesalination2791–3445450https://doi.org/10.1016/j.desal.2011.06.03510.1016/j.desal.2011.06.035Search in Google Scholar

Vazquez, M. de L., Waaub, J.P., & Ilinca, A. (2013). MCDA: Measuring robustness as a tool to address strategic wind farms issues. Green Energy and Technology, 129, 153–182. https://doi.org/10.1007/978-1-4471-5143-2_8VazquezM. de L.WaaubJ.P.IlincaA.2013MCDA: Measuring robustness as a tool to address strategic wind farms issuesGreen Energy and Technology129153182https://doi.org/10.1007/978-1-4471-5143-2_810.1007/978-1-4471-5143-2_8Search in Google Scholar

Wang, J., Hu, J., Ma, K., & Zhang, Y. (2015). A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy, 78, 374–385. https://doi.org/10.1016/j.renene.2014.12.074WangJ.HuJ.MaK.ZhangY.2015A self-adaptive hybrid approach for wind speed forecastingRenewable Energy78374385https://doi.org/10.1016/j.renene.2014.12.07410.1016/j.renene.2014.12.074Search in Google Scholar

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.WassermanS.FaustK.1994Social network analysis: Methods and applicationsCambridge University Press10.1017/CBO9780511815478Search in Google Scholar

Whiteman, A., Sohn, H., Esparrago, J., Arkhipova, I., & Elsayed, S. (2018). Renewable Capacity Statistics 2018. In /publications/2018/Mar/Renewable-Capacity-Statistics-2018. https://www.irena.org/publications/2018/Mar/Renewable-Capacity-Statistics-2018WhitemanA.SohnH.EsparragoJ.ArkhipovaI.ElsayedS.2018Renewable Capacity Statistics 2018In /publications/2018/Mar/Renewable-Capacity-Statistics-2018. https://www.irena.org/publications/2018/Mar/Renewable-Capacity-Statistics-2018Search in Google Scholar

Wu, J. (2019). Infrastructure of Scientometrics: The Big and Network Picture. Journal of Data and Information Science, 4(4), 1–12. https://doi.org/10.2478/jdis-2019-0017WuJ.2019Infrastructure of Scientometrics: The Big and Network PictureJournal of Data and Information Science44112https://doi.org/10.2478/jdis-2019-001710.2478/jdis-2019-0017Search in Google Scholar

Wuestman, M.L., Hoekman, J., & Frenken, K. (2019). The geography of scientific citations. Research Policy, 48(7), 1771–1780. https://doi.org/10.1016/j.respol.2019.04.004WuestmanM.L.HoekmanJ.FrenkenK.2019The geography of scientific citationsResearch Policy48717711780https://doi.org/10.1016/j.respol.2019.04.00410.1016/j.respol.2019.04.004Search in Google Scholar

Xu, H., Wang, C., Dong, K., Luo, R., Yue, Z., & Pang, H. (2020). A study of methods to identify industry-university-research institution cooperation partners based on innovation Chain theory. Journal of Data and Information Science, 3(2), 38–61. https://doi.org/10.2478/jdis-2018-0008XuH.WangC.DongK.LuoR.YueZ.PangH.2020A study of methods to identify industry-university-research institution cooperation partners based on innovation Chain theoryJournal of Data and Information Science323861https://doi.org/10.2478/jdis-2018-000810.2478/jdis-2018-0008Search in Google Scholar

Yang, W., Wang, J., Lu, H., Niu, T., & Du, P. (2019). Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China. Journal of Cleaner Production, 222, 942–959. https://doi.org/10.1016/j.jclepro.2019.03.036YangW.WangJ.LuH.NiuT.DuP.2019Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in ChinaJournal of Cleaner Production222942959https://doi.org/10.1016/j.jclepro.2019.03.03610.1016/j.jclepro.2019.03.036Search in Google Scholar

Zhang, W., Kleiber, W., Florita, A.R., Hodge, B.M., & Mather, B. (2019). Modeling and simulation of high-frequency solar irradiance. IEEE Journal of Photovoltaics, 9(1), 124–131. https://doi.org/10.1109/JPHOTOV.2018.2879756ZhangW.KleiberW.FloritaA.R.HodgeB.M.MatherB.2019Modeling and simulation of high-frequency solar irradianceIEEE Journal of Photovoltaics91124131https://doi.org/10.1109/JPHOTOV.2018.287975610.1109/JPHOTOV.2018.2879756Search in Google Scholar

Zhang, W., Maleki, A., Rosen, M.A., & Liu, J. (2018). Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy, 163, 191–207. https://doi.org/10.1016/j.energy.2018.08.112ZhangW.MalekiA.RosenM.A.LiuJ.2018Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storageEnergy163191207https://doi.org/10.1016/j.energy.2018.08.11210.1016/j.energy.2018.08.112Search in Google Scholar

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