This study proposes the Extreme Gradient Boosting-based Solar Photovoltaic Power Generation Prediction (XGB-SPPGP) model to predict solar irradiance and power with minimal error.
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All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering
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The integration of XAI with machine learning and deep learning technologies has markedly advanced the field of solar power generation. The proposed SPXAI model effectively tackles the unpredictability
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Solar irradiation, ambient and module temperature are key factors and important variables to estimate PV power generation. Performance of developed models was evaluated and compared to
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This study provides a paradigm for an artificial intelligence-driven hybrid solar power system, including optimized solar tracking with advanced
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At present, the photovoltaic power generation prediction model based on machine learning and deep learning has gradually become mainstream, which can effectively improve prediction accuracy and
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This study proposes a prediction model that integrates CNN and DNN to forecast photovoltaic solar power generation. The results from this study indicate that the proposed model
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This study proposes a hybrid solar power system aided by AI that incorporates high-performance solar tracking, intelligent PV technologies, and blockchain
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In this paper, we explore the impact of AI technology on PV power generation systems and its applications from a global perspective. Central to the discussion
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The spread of artificial intelligence (AI) over diverse industries provides many benefits as well as challenges. The inner working of an AI system still behaves.
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