Machine learning methods for solar power generation maximizing and forecasting

Abstract

Systems for power forecasting and maximizing of the generated electricity from a solar plant based on machine learning methods increase the efficiency of a solar plant. Therefore, these systems are relevant in accordance with the priority direction of the development of science, technology and engineering in the Russian Federation, with the priority of the state energy policy «Energy Strategy for the period up to 2035» and technological platforms approved by the government commission for high technologies and innovations: “Intelligent Electric Power System of Russia”, “Small Distributed Energy”. The solar plant systems have complex non-linear dynamics with uncertainties due to changes in system parameters and insolation fluctuations. Thus, it is impossible to approximate these complex dynamics with classical methods with a given accuracy, while machine learning methods provide the required accuracy. Machine learning methods are becoming key elements of modern systems for predicting and maximizing of the generated electricity from a solar plant due to the growing demand for high-performance data analysis to improve the efficiency and reliability of a solar plant. The systems for forecasting and maximizing of the generated electricity from a solar plant based on machine learning algorithms provide the following advantages as compared to traditional algorithms: the required accuracy of solving these problems; safe and efficient management of electrical grids that integrate solar plants. Unlike other review articles, our study: briefly summarizes our intelligent self-adapting smart systems for forecasting and maximizing of the generated electricity from a solar plant; summarizes the analytical review in tables which reflect a comparative analysis of the quality, including accuracy, for systems for forecasting and maximizing of the generated electricity from a solar plant based on machine learning algorithms; assesses the prospects for the future digital transformation of a solar power plant into an smart solar power plant based on integrated advanced technologies, including machine learning.