Forecasting solar energy generation using deep learning models

The application of deep learning models for forecasting solar energy generation is considered. An analysis and comparison of the efficiency of recurrent (LSTM, GRU), convolutional (CNN), and temporal convolutional networks (TCN) for forecasting time series of solar energy generation were conducted....

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Bibliographic Details
Published inMathematical Modeling and Computing Vol. 12; no. 2; pp. 669 - 681
Main Authors Khasyshyn, N., Liubinskyi, B.
Format Journal Article
LanguageEnglish
Published 2025
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ISSN2312-9794
2415-3788
DOI10.23939/mmc2025.02.669

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Summary:The application of deep learning models for forecasting solar energy generation is considered. An analysis and comparison of the efficiency of recurrent (LSTM, GRU), convolutional (CNN), and temporal convolutional networks (TCN) for forecasting time series of solar energy generation were conducted. The possibility of improving forecasting accuracy by constructing a hybrid model combining ARIMA and CNN was explored. The results of experiments for different EU countries are presented, and a comparison of models in terms of forecasting accuracy and computational efficiency is performed as well.
ISSN:2312-9794
2415-3788
DOI:10.23939/mmc2025.02.669