A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies

Vladimir Simankov, Pavel Buchatskiy, Anatoliy Kazak (), Semen Teploukhov, Stefan Onishchenko, Kirill Kuzmin and Petr Chetyrbok
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Vladimir Simankov: Department of Cybersecurity and Information Security, Institute of Computer Systems and Information Security, Kuban State Technological University, 350072 Krasnodar, Russia
Pavel Buchatskiy: Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia
Anatoliy Kazak: Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, 295007 Simferopol, Russia
Semen Teploukhov: Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia
Stefan Onishchenko: Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia
Kirill Kuzmin: Department of Automated Information Processing and Management Systems, Adyghe State University, 385000 Maykop, Russia
Petr Chetyrbok: Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, 295007 Simferopol, Russia

Energies, 2024, vol. 17, issue 2, 1-23

Abstract: The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

Keywords: renewable energy; artificial intelligence; digital twins; forecasting; machine learning; neural networks; LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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