Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review

Paolo Di Leo (), Alessandro Ciocia, Gabriele Malgaroli and Filippo Spertino
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Paolo Di Leo: Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Turin, Italy
Alessandro Ciocia: Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Turin, Italy
Gabriele Malgaroli: Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Turin, Italy
Filippo Spertino: Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Turin, Italy

Energies, 2025, vol. 18, issue 8, 1-28

Abstract: The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration of solar energy into power systems. This study conducts an up-to-date, systematized analysis of different models and methods used for photovoltaic power prediction. It begins with a new taxonomy, classifying PV forecasting models according to the time horizon, architecture, and selection criteria matched to certain application areas. An overview of the most popular heterogeneous forecasting techniques, including physical models, statistical methodologies, machine learning algorithms, and hybrid approaches, is provided; their respective advantages and disadvantages are put into perspective based on different forecasting tasks. This paper also explores advanced model optimization methodologies; achieving hyperparameter tuning; feature selection, and the use of evolutionary and swarm intelligence algorithms, which have shown promise in enhancing the accuracy and efficiency of PV power forecasting models. This review includes a detailed examination of performance metrics and frameworks, as well as the consequences of different weather conditions affecting renewable energy generation and the operational and economic implications of forecasting performance. This paper also highlights recent advancements in the field, including the use of deep learning architectures, the incorporation of diverse data sources, and the development of real-time and on-demand forecasting solutions. Finally, this paper identifies key challenges and future research directions, emphasizing the need for improved model adaptability, data quality, and computational efficiency to support the large-scale integration of PV power into future energy systems. By providing a holistic and critical assessment of the PV power forecasting landscape, this review aims to serve as a valuable resource for researchers, practitioners, and decision makers working towards the sustainable and reliable deployment of solar energy worldwide.

Keywords: photovoltaic power forecasting; solar energy; forecasting models; machine learning; hybrid approaches; optimization strategies; performance evaluation; future directions (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: 2025
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