<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Nowadays, the renewable energy sector is an area of interest for every state. Global regulations and policies encourage the development of these technologies, given the current political context, but also environmental issues. Romania, due to its geographical position and climate, is considered a country with high potential regarding the implementation of alternative sources of renewable energy. This research presents the importance of solar energy and provides a statistical analysis on the sectors influencing the implementation of green energy. At the same time, those counties that are eligible are identified and different scenarios are created for the ineligible counties that lead to their eligibility.</p>
<p>The research develops 3 main objectives. To begin with, it is desired to be created an overview of the indicators included in the analysis, in order to develop a detailed statistical analysis of the situation of each county of Romania. Following this extracted information, the second objective is outlined, which is to create an indicator that groups counties into counties eligible for solar energy and counties ineligible for solar energy using the K-Means Cluster-unsupervised learning algorithm. Finally, using the supervised learning algorithm - Logistic Regression, predictions will be made with the help of which those sectors of activity that can be improved in order to implement green energy will be identified.</p>
</abstract>

Nowadays, the renewable energy sector is an area of interest for every state. Global regulations and policies encourage the development of these technologies, given the current political context, but also environmental issues. Romania, due to its geographical position and climate, is considered a country with high potential regarding the implementation of alternative sources of renewable energy. This research presents the importance of solar energy and provides a statistical analysis on the sectors influencing the implementation of green energy. At the same time, those counties that are eligible are identified and different scenarios are created for the ineligible counties that lead to their eligibility.
The research develops 3 main objectives. To begin with, it is desired to be created an overview of the indicators included in the analysis, in order to develop a detailed statistical analysis of the situation of each county of Romania. Following this extracted information, the second objective is outlined, which is to create an indicator that groups counties into counties eligible for solar energy and counties ineligible for solar energy using the K-Means Cluster-unsupervised learning algorithm. Finally, using the supervised learning algorithm - Logistic Regression, predictions will be made with the help of which those sectors of activity that can be improved in order to implement green energy will be identified.

Efficient Capture of Solar Energy in Romania: Approach in Territorial Profile Using Predictive Statistical Techniques

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution 4.0 International License.

Nowadays, the renewable energy sector is an area of interest for every state. Global regulations and policies encourage the development of these technologies,...

Efficient Capture of Solar Energy in Romania: Approach in Territorial Profile Using Predictive Statistical Techniques

Online veröffentlicht: 14. Juli 2023

Seitenbereich: 1519 - 1533

DOI: https://doi.org/10.2478/picbe-2023-0137

Schlüsselwörter
Renewable Energy, Solar Energy, Territorial analysis, Predictive analysis

© 2023 Cătălin-Laurențiu Rotaru et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Efficient Capture of Solar Energy in Romania: Approach in Territorial Profile Using Predictive Statistical Techniques

Online veröffentlicht: 14. Juli 2023

Seitenbereich: 1519 - 1533

DOI: https://doi.org/10.2478/picbe-2023-0137

SchlüsselwörterRenewable Energy, Solar Energy, Territorial analysis, Predictive analysis

© 2023 Cătălin-Laurențiu Rotaru et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Renewable Energy, Solar Energy, Territorial analysis, Predictive analysis