Forecasting hourly electricity generation by a solar power plant using machine learning algorithms - Anzhelika D. Morgoeva; Irbek D. Morgoev; Roman V. Klyuev; Svetlana S. Kochkovskaya (Известия Томского политехнического университета: Инжиниринг георесурсов, 2023)
Relevance. The need to develop energy-saving approaches through the use of data mining tools to improve the efficiency of management decision-making and more optimal use of energy resources. Forecasting the amount of electric energy generated by a solar power plant will allow optimal electricity distribution in decentralized systems. Information about the amount of electricity generated by a solar power plant and transmitted to a grid at every hour will allow planning the use of generated electricity and its distribution more reasonably. Also, the presence of a reliable forecast will allow embedding a predictive model into the micro-grid management subsystem. This will facilitate the integration of centralized electrical networks and distributed generation facilities. Aim. To analyze scientific papers containing proposals to improve the accuracy of determining the amount of electricity generated by solar power plants; to create machine learning models that allow you to create a short-term forecast of electricity generation by a solar power plant. Objects. Solar power plant named after A.A. Vlaznev (Sakmarskaya SPP), Orenburg region, Orsk, Oktyabrsky district, geographical coordinates: 51.266023, 58.474689. Methods. Analytical method, methods of mathematical statistics, methods of machine learning, complex generalization of scientific achievements and practical experience in the use of data processing tools in the tasks of forecasting electricity generation by solar power plants. Results. The authors have carried out the review of literature sources reflecting the results of using modern data mining tools in predicting the magnitude of electricity generation by solar power plants. The paper considers various approaches to forecasting electricity generation at solar power plants. The analysis of factors used in forecasting is carried out. The authors obtained the results of theoretical and applied nature. The results  consist in recommendations on using exogenous variables in predicting power generation at SPP, as well as some machine learning algorithms in construction of predictive models. These recommendations were obtained in generalizing the results of the applied research.