1. bookVolume 11 (2020): Issue 1 (February 2020)
Journal Details
License
Format
Journal
First Published
12 Dec 2015
Publication timeframe
1 time per year
Languages
English
access type Open Access

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Published Online: 20 Feb 2020
Page range: 51 - 76
Received: 23 Dec 2019
Journal Details
License
Format
Journal
First Published
12 Dec 2015
Publication timeframe
1 time per year
Languages
English
Abstract

Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

Keywords

[1] Y. Lin, H. Luo, D. Wang, H. Guo, and K. Zhu, “An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting,” Energies, vol. 10, no. 1186, 2017.Search in Google Scholar

[2] M. D. Reddy, “Load Forecasting using Linear Regression Analysis in Time series model for RGUKT, R.K. Valley Campus HT Feeder,” International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 5, 2017.Search in Google Scholar

[3] G. Nalcaci, A. Özmen, and G. W. Weber, “Long-term Load Forecasting: Models Based on MARS, ANN and LR methods,” Central European Journal of Operations Research (CEJOR), Springer-Verlag GmbH Germany, vol. 27, no. 2019, pp. 1033–1049, 2018.Search in Google Scholar

[4] E. Almeshaiei and H. Soltan, “A Methodology for Electric Power Load Forecasting,” Alexandria Engineering Journal, vol. 50, no. 2011, pp. 137–144, 2011.Search in Google Scholar

[5] J. Zhang, “Research on Power Load Forecasting Based on the Improved Elman Neural Network,” The Italian Association of Chemical Engineering (AIDIC), vol. 51, no. 2016, pp. 589-594, 2016.Search in Google Scholar

[6] M. Y. Khamaira, A. S. Krzma, and A. M. Alnass, “Long Term Peak Load Forecasting for the Libyan Network,” in Conference for Engineering Sciences and Technology (CEST), 2018, vol. 1, pp. 185-193: AIJR Publisher.Search in Google Scholar

[7] O. Demirel, A. Kakilli, and M. Tektas, “Electric Energy Load Forecasting Using ANFIS and ARMA Methods,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 25, no. 3, pp. 601-610, 2010.Search in Google Scholar

[8] Q. Wang, Y. Wang, and L. Zhang, “Research on Post-Evaluation Model and Method of Electric Powe Demand Forecasting,” presented at the Chinese Control and Decision Conference (CCDC), China, 2008.Search in Google Scholar

[9] X. Zhanga, J. Wanga, and K. Zhang, “Short-Term Electric Load Forecasting Based on Singular Spectrum Analysis and Support Vector Machine Optimized by Cuckoo Search Algorithm,” Electric Power Systems Research, vol. 146, no. 2017, pp. 270–285, 2017.Search in Google Scholar

[10] Ü. B. Filik, Ö. N. Gerek, and M. Kurban, “A Novel Modeling Approach for Hourly Forecasting of Long-Term Electric Energy Demand,” Energy Conversion and Management, vol. 52, no. 2011, pp. 199–211, 2011.Search in Google Scholar

[11] R. Gordillo-Orquera, L. M. Lopez-Ramos, S. Muñoz-Romero, P. Iglesias-Casarrubios, D. Arcos-Avilés, A. G. Marques, and J. L. Rojo-Álvarez, “Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings,” Energies, vol. 11, no. 493, 2018.Search in Google Scholar

[12] L. Friedrich and A. Afshari, “Short-Term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables,” presented at the 7th International Conference on Applied Energy (ICAE), 2015.Search in Google Scholar

[13] N. Abu-Shikhah and F. Elkarmi, “Medium-Term Electric Load Forecasting Using Singular Value Decomposition,” Energy Conversion and Management, vol. 36, no. 7, pp. 4259-4271, 2011.Search in Google Scholar

[14] R. Wanga, J. Wangb, and Y. Xu, “A Novel Combined Model Based on Bybrid Optimization Algorithm for Electrical Load Forecasting,” Applied Soft Computing Journal, vol. 82, no. 2019, p. 105548, 2019.Search in Google Scholar

[15] C. Kuster, Y. Rezgui, and M. Mourshed, “Electrical Load Forecasting Models: A Critical Systematic Review,” in Sustainable Cities and Society, ed, 2017.Search in Google Scholar

[16] H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: Literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23–34, 2002.Search in Google Scholar

[17] C. Kuster, Y. Rezgui, and M. Mourshed, “Electrical load forecasting models: A critical systematic review,” Sustainable Cities and Society, vol. 35, pp. 257–270, 2017, Art. no. Pii: s2210670717305899.Search in Google Scholar

[18] A. K. Singh, Ibraheem, S. Khatoon, M. Muazzam, and D. K. Chaturvedi, “Load forecasting techniques and methodologies: A review,” in 2012 2nd International Conference on Power, Control and Embedded Systems, 2012, pp. 1–10.Search in Google Scholar

[19] S. Fallah, M. Ganjkhani, S. Shamshirband, and K.-w. Chau, “Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview,” Energies, vol. 12, no. 3, p. 393, 2019, Art. no. PII: en12030393.Search in Google Scholar

[20] W. C. Hong, M. W. Li, and G. F. Fan, Short-Term Load Forecasting by Artificial Intelligent Technologies. MDPI AG, 2019.Search in Google Scholar

[21] I. A. b. W. A. Razak, S. b. Majid, M. S. b. M. Aras, and A. b. Ahmad, “Electricity Load Forecasting Using Data Mining Technique,” ed: IntechOpen, 2012.Search in Google Scholar

[22] F. Su, Y. Xu, and X. Tang, “Short-and mid-term load forecasting using machine learning models,” in 2017 China International Electrical and Energy Conference (CIEEC), 2017, pp. 406–411.Search in Google Scholar

[23] S. HemaChandra, V. Harish, C. R. Kumar, and V. Nagarjuna, “A Review of Long Term Electrical Load Forecasting Methods,” (in English), Artificial Intelligent Systems and Machine Learning, vol. 4, no. 10, pp. 566–569, 2012.Search in Google Scholar

[24] S. K. Panda, S. N. Mohanty, and A. K. Jagadev, “Long Term Electrical Load Forecasting: An Empirical Study across Techniques and Domains,” Indian Journal of Science and Technology, vol. 10, no. 26, pp. 1–16, 2017.Search in Google Scholar

[25] M. Jacob, C. Neves, and D. Vukadinović Greetham, “Short Term Load Forecasting,” in FORECASTING AND ASSESSING RISK OF INDIVIDUAL ELECTRICITY PEAKS, vol. 33, M. N. C. V. G. D. Jacob, Ed. (Mathematics of Planet Earth, [S.l.]: SPRINGER NATURE, 2019, pp. 15–37.Search in Google Scholar

[26] R. Weron, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. Wiley, 2007.Search in Google Scholar

[27] B. Yildiz, J. I. Bilbao, and A. B. Sproul, “A review and analysis of regression and machine learning models on commercial building electricity load forecasting,” Renewable and Sustainable Energy Reviews, vol. 73, pp. 1104–1122, 2017, Art. no. Pii: s1364032117302265.Search in Google Scholar

[28] E. A. Feinberg and D. Genethliou, “Load Forecasting,” in Applied Mathematics for Restructured Electric Power Systems.Optimization, Control, and Computational Intelligence, J. H. Chow, F. F. Wu, and J. A. Momoh, Eds. Dordrecht: Springer-Verlag New York Inc, 2006, pp. 269-285.Search in Google Scholar

[29] S. A.-h. Soliman and A. M. Al-Kandari, Electrical Load Forecasting: Modeling and Model Construction, 1st ed. Butterworth–Heineman, 2010.Search in Google Scholar

[30] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. OTexts: Melbourne, Australia, 2018.Search in Google Scholar

[31] R.M.Dawes, “Clinical versus Actuarial Prediction,” International Encyclopedia of the Social & Behavioral Sciences, pp. 2048-2051, 2001. Elsevier Ltd.Search in Google Scholar

[32] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: Forecasting and control / George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, 4th ed. ed. (Wiley series in probability and statistics). Oxford: Wiley, 2008.Search in Google Scholar

[33] P. S. P. Cowpertwait and A. V. Metcalfe, Introductory Time Series with R. Springer New York, 2009.Search in Google Scholar

[34] A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, Power Generation, Operation, and Control. Wiley, 2013.Search in Google Scholar

[35] Casals, Jose, Garcia-Hiernaux, Alfredo, Jerez, Miguel, Sotoca, Sonia, Trindade, and A. Alexandre, “State-Space Methods for Time Series Analysis: Theory, Applications and Software.”Search in Google Scholar

[36] J. J. F. Commandeur and S. J. Koopman, An introduction to state space time series analysis (Practical econometrics). Oxford; New York: Oxford University Press, 2007, pp. xiv, 174.Search in Google Scholar

[37] J. Durbin and S. J. Koopman, Time series analysis by state space methods, 2nd ed. ed. (Oxford statistical science series, no. 38). Oxford: Oxford University Press, 2012, pp. xxi, 346.Search in Google Scholar

[38] L. Huang, Y. Yang, H. Zhao, X. Wang, and H. Zheng, “Time series modeling and filtering method of electric power load stochastic noise,” Protection and Control of Modern Power Systems, vol. 2, no. 1, p. 7, 2017, Art. no. Pii: 59.Search in Google Scholar

[39] S. Markoulakis, “Short-term load forecasting based on the Kalman filter and the neural-fuzzy network (ANFIS).”Search in Google Scholar

[40] M. Gaur and A. Majumdar, “One-Day-Ahead Load Forecasting using nonlinear Kalman filtering algorithms,” 2016.Search in Google Scholar

[41] J. Cheng, W. Xiong, and L. Ai, “Electric Load Forecasting Based on Improved Grey Neural Network,” in Recent advances in computer science and information engineering, vol. 124, Z. Qian, Ed. (Lecture Notes in Electrical Engineering, no. 124-129) Heidelberg: Springer, 2012, pp. 651–655.Search in Google Scholar

[42] C. Herui, B. Tao, and L. Yanzi, “Short-term Power Load Forecasting Based on Gray Theory,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 11, no. 11, 2013.Search in Google Scholar

[43] M. Jin, X. Zhou, Z. M. Zhang, and M. M. Tentzeris, “Short-term power load forecasting using grey correlation contest modeling,” Expert Systems with Applications, vol. 39, no. 1, pp. 773–779, 2012, Art. no. Pii: s0957417411010347.Search in Google Scholar

[44] J. A. S. Kelso, P. Érdi, K. J. Friston, H. Haken, J. Kacprzyk, J. Kurths, L. E. Reichl, P. Schuster, F. Schweitzer, D. Sornette, S. Liu, and Y. Lin, Grey Systems (no. 68). Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, p. 401.Search in Google Scholar

[45] S. Liu and Y. Lin, Grey systems: Theory and applications / Sifeng Liu and Yi Lin (Understanding complex systems). Berlin: Springer Verlag, 2010.Search in Google Scholar

[46] S. Liu, Y. Yang, and J. Forrest, Grey data analysis: Methods, models and applications / Sifeng Liu, Yingjie Yang, Jeffrey Forrest (Computational risk management). Singapore: Springer, 2017.Search in Google Scholar

[47] Y. Lu, Y. Teng, and H. Wang, “Load Prediction in Power System with Grey Theory and its Diagnosis of Stabilization,” Electric Power Components and Systems, vol. 47, no. 6-7, pp. 619–628, 2019.Search in Google Scholar

[48] J. Mi, L. Fan, X. Duan, and Y. Qiu, “Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model,” Mathematical Problems in Engineering, vol. 2018, no. 1, pp. 1–11, 2018, Art. no. Pii: 3894723.Search in Google Scholar

[49] T. Ozcan, T. Küçükdeniz, and F. H. Sezgin, “Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting,” in Nature inspired computing, vol. 1, I. R. Management Association, Ed. Hershey PA: IGI Global, 2017, pp. 1161–1183.Search in Google Scholar

[50] H. Zhao and S. Guo, “An optimized grey model for annual power load forecasting,” Energy, vol. 107, pp. 272–286, 2016, Art. no. Pii: s0360544216304066.Search in Google Scholar

[51] P. Ji, D. Xiong, P. Wang, and J. Chen, “A Study on Exponential Smoothing Model for Load Forecasting,” presented at the Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2012.27-29 March 2012, Shanghai, China; proceedings, Piscataway, NJ, 2012. Available: http://ieeexplore.ieee.org/document/6307555/Search in Google Scholar

[52] D. Dragan, T. Kramberger, and M. Intihar, “A comparison of Methods for Forecasting the Container Throughput in North Adriatic Ports,” presented at the IAME 2014, Norfolk, 2014.Search in Google Scholar

[53] D. Dragan, A. Keshavarzsaleh, T. Kramberger, B. Jereb, and M. Rosi, “Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains,” Logistics & Sustainable Transport, vol. 10, no. 1, pp. 11–30, 2019.Search in Google Scholar

[54] R. J. Hyndman, Forecasting with exponential smoothing. Berlin; London: Springer, 2008.Search in Google Scholar

[55] R. Weron, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. England: John Wiley & Sons Ltd, 2006.Search in Google Scholar

[56] R. Adhikari and R. K. Agrawal, An Introductory Study on Time Series Modeling and Forecasting. LAP Lambert Academic Publishing, 2013.Search in Google Scholar

[57] J. G.Jetcheva, MostafaMajidpour, and Wei-PengChen, “Neural Network Model Ensembles for Building Level Electricity Load Forecasts,” Energy andBuildings, vol. 84, no. 2014, pp. 214–223, 2014.Search in Google Scholar

[58] M. Sarhani and A. E. Afia, “Electric Load Forecasting Using Hybrid Machine Learning Approach Incorporating Feature Selection,” in International Conference on Big Data Cloud and Applications, Tetuan, Morocco, 2015.Search in Google Scholar

[59] X. Wang, K. Smith-Miles, and R. Hyndman, “Rule Induction for Forecasting Method Selection: Meta-Learning the Characteristics of Univariate Time Series,” Neurocomputing, vol. 72, no. 10-12, pp. 2581-2594, 2009.Search in Google Scholar

[60] M. Intihar, T. Kramberger, and D. Dragan, “Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model,” PROMET - Traffic&Transportation, vol. 29, no. 5, pp. 529–542, 2017.Search in Google Scholar

[61] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina, Chapel Hill2004, vol. TR 95-041.Search in Google Scholar

[62] E. Kayacan, B. Ulutas, and O. Kaynak, “Grey system theory-based models in time series prediction,” Expert Systems with Applications, vol. 37, no. 2, pp. 1784-1789, 2010.Search in Google Scholar

[63] Y. Feng, “Study on Medium and Long Term Power Load Forecasting Based on Combination Forecasting Model,” Chemical Engineering Transactions, vol. 51, no. 2015, pp. 859-864, 2015.Search in Google Scholar

[64] E. Ostertagová and O. Ostertag, “The Simple Exponential Smoothing Model,” presented at the Modelling of Mechanical and Mechatronic Systems 2011: The 4th International conference, Faculty of Mechanical engineering, Technical university of Košice, 2011.Search in Google Scholar

[65] A. Chusyairi, R. N. S. Pelsri, and Bagio, “The Use of Exponential Smoothing Method to Predict Missing Service E-Report,” presented at the Information Systems and Electrical Engineering (ICITISEE): 2nd International Conferences on Information Technology, 2017.Search in Google Scholar

[66] M. A. Momani, W. H. Alrousan, and A. T. Alqudah, “Short-Term Load Forecasting Based on NARX and Radial Basis Neural Networks Approaches for the Jordanian Power Grid “ Jordan Journal of Electrical Engineering, vol. 2, no. 1, pp. 81-93, 2016.Search in Google Scholar

[67] L. C. M. d. Andrade, M. Oleskovicz, A. Q. Santos, D. V. Coury, and R. A. S. Fernandes, “Very short-term load forecasting based on NARX recurrent neural networks,” in 2014 IEEE PES general meetingPiscataway, NJ: IEEE, 2014, pp. 1–5.Search in Google Scholar

[68] J. Buitrago and S. Asfour, “Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs,” Energies, vol. 10, no. 1, p. 40, 2017, Art. no. PII: en10010040.Search in Google Scholar

[69] W. X. Jiatang Cheng and L. Ai, “LNEE 124 - Electric Load Forecasting Based on Improved Grey Neural Network.”Search in Google Scholar

[70] H. Li, Y. Zhu, J. Hu, and Z. Li, “A localized NARX Neural Network model for Short-term load forecasting based upon Self-Organizing Mapping,” in 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), I. I. F. E. E. Conference, Ed. [Piscataway, NJ]: IEEE, 2017, pp. 749–754.Search in Google Scholar

[71] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006.Search in Google Scholar

[72] Y. Chen, M. Kloft, Y. Yang, C. Li, and L. Li, “Mixed kernel based extreme learning machine for electric load forecasting,” Neurocomputing, vol. 312, pp. 90-106, 2018.Search in Google Scholar

[73] S. K. Dash and D. Patel, “Short-term electric load forecasting using Extreme Learning Machine - a case study of Indian power market,” presented at the 2015 IEEE Power, Communication and Information Technology Conference (PCITC.15-17 October, 2015, Siksha ‘O’ Anusandhan University, Bhubaneswar, India : PCITC-2015 proceedings, [Piscataway, NJ], 2015. Available: http://ieeexplore.ieee.org/document/7438135/Search in Google Scholar

[74] Ö. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,” International Journal of Electrical Power & Energy Systems, vol. 78, pp. 429-435, 2016.Search in Google Scholar

[75] C. H. Weng, W. Ting, L. Xueyong, and R. Weerasinghe, “Research on short-term electric load forecasting based on extreme learning machine,” E3S Web of Conferences, vol. 53, p. 02009, 2018.Search in Google Scholar

[76] P. J. Garcia-Laencina, “Improving Predictions Using Linear Combination Of Multiple Extreme Learning Machines,” Information Technology And Control, vol. 42, no. 1, 2013.Search in Google Scholar

[77] M. A. A. Albadr and S. Tiun, “Extreme Learning Machine: A Review “ International Journal of Applied Engineering Research, vol. 12, no. 14, pp. 4610-4623, 2017.Search in Google Scholar

[78] W. Ting and L. Xueyong, “Research on short-term electric load forecasting based on extreme learning machine,” E3S Web of Conferences, vol. 53, p. 02009, 2018.Search in Google Scholar

[79] Y. Wei, H. Huang, B. Chen, B. Zheng, and Y. Wang, “Application of Extreme Learning Machine for Predicting Chlorophylla Concentration Inartificial Upwelling Processes,” Mathematical Problems in Engineering, vol. 2019, pp. 1-11, 2019.Search in Google Scholar

[80] Z. Yang, T. Zhang, J. Lu, Y. Su, D. Zhang, and Y. Duan, “Extreme learning machines for regression based on V-matrix method,” Cogn Neurodyn, vol. 11, no. 5, pp. 453-465, Oct 2017.Search in Google Scholar

[81] Y. Fu, Z. Li, H. Zhang, and P. Xu, “Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices,” Procedia Engineering, vol. 121, pp. 1016-1022, 2015.Search in Google Scholar

[82] W.-C. Hong, “Electric load forecasting by support vector model,” Applied Mathematical Modelling, vol. 33, no. 5, pp. 2444-2454, 2009.Search in Google Scholar

[83] Z. Hu, Y. Bao, and T. Xiong, “Electricity load forecasting using support vector regression with memetic algorithms,” ScientificWorldJournal, vol. 2013, p. 292575, 2013.Search in Google Scholar

[84] S. Qiang and Y. Pu, “Short-term power load forecasting based on support vector machine and particle swarm optimization,” Journal of Algorithms & Computational Technology, vol. 13, p. 174830181879706, 2018.Search in Google Scholar

[85] S. Maldonado, A. González, and S. Crone, “Automatic time series analysis for electric load forecasting via support vector regression,” Applied Soft Computing, vol. 83, p. 105616, 2019.Search in Google Scholar

[86] N. Cristianini and J. Shawe-Taylor, An introduction to Support Vector Machines. New York: Cambridge University Press, 2000.Search in Google Scholar

[87] N. Deng, Y. Tian, and C. Zhang, Support vector machines (Chapman & Hall/CRC data mining and knowledge discovery series). Boca Raton: CRC Press, 2013.Search in Google Scholar

[88] I. Steinwart and A. Christmann, Support vector machines (Information science and statistics). New York: Springer, 2008.Search in Google Scholar

[89] J. A. K. Suykens, Least squares support vector machines. New Jersey; London: World Scientific H1 - British Library H2 - DSCm03/18809, 2002.Search in Google Scholar

[90] A. Abraham and S. Das, Computational Intelligence in Power Engineering. Springer Berlin Heidelberg, 2010.Search in Google Scholar

[91] P. L. Anderson, Business Economics and Finance with MATLAB, GIS, and Simulation Models. CRC Press, 2004.Search in Google Scholar

[92] M. F. Azeem, Fuzzy Inference System: Theory and Applications. IntechOpen, 2012.Search in Google Scholar

[93] J. H. Chow, F. F. Wu, and J. A. Momoh, Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence. Springer US, 2004.Search in Google Scholar

[94] S. K. Halgamuge and L. Wang, Computational Intelligence for Modelling and Prediction. Springer Berlin Heidelberg, 2005.Search in Google Scholar

[95] R. Jensen and Q. Shen, Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley, 2008.Search in Google Scholar

[96] S. Kalogirou, Artificial Intelligence in Energy and Renewable Energy Systems. Nova Science Publishers, 2007.Search in Google Scholar

[97] A. Konar and D. Bhattacharya, Time-Series Prediction and Applications: A Machine Intelligence Approach. Springer International Publishing, 2017.Search in Google Scholar

[98] E. Ogliari and S. Leva, Computational Intelligence in Photovoltaic Systems. Mdpi AG, 2019.Search in Google Scholar

[99] A. K. Palit and D. Popovic, Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer London, 2006.Search in Google Scholar

[100] M. Paulescu, E. Paulescu, P. Gravila, and V. Badescu, Weather Modeling and Forecasting of PV Systems Operation. Springer London, 2012.Search in Google Scholar

[101] W. Pedrycz and S. M. Chen, Time Series Analysis, Modeling and Applications: A Computational Intelligence Perspective. Springer Berlin Heidelberg, 2012.Search in Google Scholar

[102] S. A. Soliman and A. M. Al-Kandari, Electrical Load Forecasting: Modeling and Model Construction. Elsevier Science, 2010.Search in Google Scholar

[103] Y. H. Song, Modern Optimisation Techniques in Power Systems. Springer Netherlands, 1999.Search in Google Scholar

[104] M. Sudha, Applied Computational Intelligence. Educreation Publishing, 2019.Search in Google Scholar

[105] G. Tayfur, Soft Computing in Water Resources Engineering: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms. WIT Press, 2014.Search in Google Scholar

[106] K. E. Voges and N. Pope, Business Applications and Computational Intelligence. Idea Group Publishing, 2006.Search in Google Scholar

[107] J. Wang, Business Intelligence in Economic Forecasting: Technologies and Techniques: Technologies and Techniques. Information Science Reference, 2010.Search in Google Scholar

[108] L. Wang, C. Singh, and A. Kusiak, Wind Power Systems: Applications of Computational Intelligence. Springer Berlin Heidelberg, 2010.Search in Google Scholar

[109] P. P. Wang, Computational Intelligence in Economics and Finance. Springer Berlin Heidelberg, 2013.Search in Google Scholar

[110] A. T. Ali, E. B. Tayeb, and Z. M. Shamseldin, “Short term electrical load forecasting using fuzzy logic,” International Journal Of Advancement In Engineering Technology, Management and Applied Science (IJAETMAS), vol. 3, 2016.Search in Google Scholar

[111] D. Ali, M. Yohanna, M. I. Puwu, and B. M. Garkida, “Long-term load forecast modelling using a fuzzy logic approach,” Pacific Science Review A: Natural Science and Engineering, vol. 18, no. 2, pp. 123-127, 2016.Search in Google Scholar

[112] M. Faysal, M. J. Islam, M. M. Murad, M. I. Islam, and M. R. Amin, “Electrical Load Forecasting Using Fuzzy System,” Journal of Computer and Communications, vol. 07, no. 09, pp. 27-37, 2019.Search in Google Scholar

[113] M. K. Singla and S. Hans, “Load Forecasting using Fuzzy Logic Tool Box,” Global Research and Development Journal for Engineering, vol. 38, pp. 12-19, 2018.Search in Google Scholar

[114] L. Yao, Y.-l. Jiang, and J. Xiao, “Short-Term Power Load Forecasting by Interval Type-2 Fuzzy Logic System,” in Information Computing and Applications, Berlin, Heidelberg, 2011, pp. 575-582: Springer Berlin Heidelberg.Search in Google Scholar

[115] Z. Ismail and R. Mansor, “Fuzzy Logic Approach for Forecasting Half-hourly Electricity Load Demand,” Fuzzy Logic Approach for Forecasting Half-hourly Electricity Load Demand, 09/14 2011.Search in Google Scholar

[116] J. Jamaaluddin, D. Hadidjaja, I. Sulistiyowati, E. A. Suprayitno, I. Anshory, and S. Syahrorini, “Very short term load forecasting peak load time using fuzzy logic,” IOP Conference Series: Materials Science and Engineering, vol. 403, p. 012070, 2018.Search in Google Scholar

[117] J. Kaur and Y. S. Brar, “Short term load forecasting using fuzzy logic of 220KV transmission line,” Int. J. Eng. Res. Technol, vol. 3, no. 2278, p. e0181, 2014.Search in Google Scholar

[118] A. Laouafi, M. Mordjaoui, and T. E. Boukelia, “An adaptive neuro-fuzzy inference system-based approach for daily load curve prediction,” Journal of Energy Systems, pp. 115-126, 2018.Search in Google Scholar

[119] S. K. Patel and S. Sharma, “A Review of very Short-Term Load Forecasting (STLF) using Wavelet Neural Networks,” International Journal of Science, Engineering and Technology Research vol. 4, no. 2, 2015.Search in Google Scholar

[120] M. Mitchell, An introduction to genetic algorithms, 7. print ed. (A Bradford book). Cambridge, Mass., 2001, p. 209.Search in Google Scholar

[121] R. L. Haupt and S. E. Haupt, Practical genetic algorithms, 2nd ed. ed. Hoboken, N.J.; Chichester: Wiley-Interscience, 2004.Search in Google Scholar

[122] J. Carr, “An Introduction to Genetic Algorithms,” 2014.Search in Google Scholar

[123] C.-C. Hsu, C.-H. Wu, S.-C. Chen, and K.-L. Peng, “Dynamically Optimizing Parameters in Support Vector Regression: An Application of Electricity Load Forecasting,” presented at the System Sciences, the 39th Annual Hawaii International Conference, 2006.Search in Google Scholar

[124] D. Beasley, D. R. Bull, and R. R. Martin, “An Overview of Genetic Algorithms : Part 1, Fundamentals,” University Computing, vol. 15, no. 2, pp. 56-69, 1993.Search in Google Scholar

[125] Y. K. Al-Douri, H. Al-Chalabi, and J. Lundberg, “Time Series Forecasting using Genetic Algorithm,” in The Twelfth International Conference on Advanced Engineering Computing and Applications in Sciences, 2018.Search in Google Scholar

[126] R. R. B. de Aquino, O. N. Neto, M. M. S. Lira, A. A. Ferreira, and K. F. Santos, “Using Genetic Algorithm to Develop a Neural-Network-Based Load Forecasting,” in Artificial Neural Networks – ICANN 2007, Berlin, Heidelberg, 2007, pp. 738-747: Springer Berlin Heidelberg.Search in Google Scholar

[127] A. Gupta and P. K. Sarangi, “Electrical load forecasting using genetic algorithm based back-propagation method,” ARPN Journal of Engineering and Applied Sciences, vol. 7, no. 8, pp. 1017-1020, 2012.Search in Google Scholar

[128] G. M. Khan, F. Zafari, and S. A. Mahmud, “Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN),” in 2013 12th International Conference on Machine Learning and Applications, 2013, vol. 2, pp. 152-155.Search in Google Scholar

[129] F. Li and X. Zhao, “The application of genetic algorithm in power short-term load forecasting,” in 2012 International Conference on Image, Vision and Computing (ICIVC 2012), 2012.Search in Google Scholar

[130] J. I. Silva-Ortegaa, B. Cervantes-Bolivarb, I. A. Isaac-Millanc, Y. Cardenas-Escorciab, and G. Valencia-Ochoad, “Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system,” CHEMICAL ENGINEERING, vol. 67, 2018.Search in Google Scholar

[131] H. Verdejo, A. Awerkin, C. Becker, and G. Olguin, “Statistic Linear Parametric Techniques for Residential Electric Energy Demand Forecasting: A Review and An Implementation to Chile,” Renewable and Sustainable Energy Reviews, vol. 74, no. 2017, pp. 512–521, 2017.Search in Google Scholar

[132] M. Singla, “Load Forecasting Using Artificial Neural Network,” Thapar Institute, 2018.Search in Google Scholar

[133] J. Buitrago, “Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Multivariable Inputs,” Open Access Dissertations, 2017.Search in Google Scholar

[134] M. Shepero, “Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting,” ed: Uppsala universitet, 2018.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo