Artificial Intelligence to Predict Solar Energy Production: Risks and Economic Efficiency
DOI:
https://doi.org/10.57125/FEL.2024.06.25.06Keywords:
artificial intelligence, data analysis, energy efficiency, sustainable development, predictive algorithms, production optimization, renewable energy, solar energyAbstract
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 provide excellent opportunities for the introduction of artificial intelligence in the process of predicting the production of solar energy. However, this topic is still relatively unexplored in the international scientific community, which makes this study relevant. This study aims to analyse the impact of artificial intelligence on solar energy production forecasting. The method of this study was a systematic review. The search for sources was conducted in the Google Scholar, Springer Link, and EBSCO databases from 2019 to 2024, allowing for a focus on the latest publications. As a result of applying PRISMA guidelines, 18 publications that fully met the inclusion criteria were selected. As a result of the study, it was possible to establish that artificial intelligence has great potential for its implementation in forecasting solar energy production. The study established random prediction models and machine learning models based on artificial intelligence for their cost effectiveness and risk in 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 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 forecasting solar energy production is much more economically efficient than the risk aspects.
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