Artificial Intelligence to Predict Solar Energy Production: Risks and Economic Efficiency

Abstract

In the context of sustainable development and the increasing shift to non-fossil alternative energy sources, solar energy offers countless advantages for its conversion into electricity. Modern technologies offer great opportunities for the introduction of artificial intelligence in the process of predicting the production of solar energy. However, this topic is still quite unexplored in the international scientific community, which makes this study relevant. The purpose of this study is to analyze the impact of artificial intelligence on solar energy production forecasting. To achieve the purpose of the study, a systematic literature analysis method was applied. As a result of the study, it was possible to establish that artificial intelligence has great potential for its implementation in the process of forecasting solar energy production. The study was able to establish random prediction models and machine learning models based on artificial intelligence for their cost effectiveness and risk in the process of forecasting solar energy production. During the literature review, it became clear that the following four models are the most effective in the work: the RFR, LIME, ELI5 and SHAP. Each model has its own advantages and disadvantages. These are manifested in production management, forecasting with high speed, flexibility, and explanation, reducing the risk of variability. However, the cost-effectiveness of implementing artificial intelligence in the process of forecasting solar energy production has much more economic efficiency than the risk aspects.