Multi-Step Sky Image Prediction Using Cluster-Specific Convolutional Neural Networks for Solar Forecasting Applications

Stylianos P. Schizas, Markos A. Kousounadis-Knousen, Francky Catthoor and Pavlos S. Georgilakis ()
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Stylianos P. Schizas: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Markos A. Kousounadis-Knousen: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Francky Catthoor: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece
Pavlos S. Georgilakis: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 15780 Athens, Greece

Energies, 2025, vol. 18, issue 21, 1-23

Abstract: Effective integration of photovoltaic (PV) systems into electric power grids presents significant challenges due to the inherent variability in solar energy. Therefore, accurate PV power forecasting in various timescales is critical for the reliable operation of modern electric power systems. For short-term horizons, the primary source of solar power stochasticity is cloud movement and deformation, which are typically captured at high spatiotemporal resolutions using ground-based sky images. In this paper, we propose a novel multi-step sky image prediction framework for improved cloud tracking, which can be deployed for short-term PV power forecasting. The proposed method is based on deep learning, but instead of being purely data-driven, we propose a hybrid approach where we combine Auto-Encoder-like Convolutional Neural Networks (AE-like CNNs) with physics-informed sky image clustering to enhance robustness towards fast-varying sky conditions and effectively model non-linearities without adding to the computational overhead. The proposed method is compared against several state-of-the-art approaches using a real-world case study comprising minutely sky images. The experimental results show improvements of up to 17.97% on structural similarity and 62.14% on mean squared error, compared to persistence. These findings demonstrate that by combining effective physics-informed preprocessing with deep learning, multi-step ahead sky image forecasting can be reliably achieved even at low temporal resolutions.

Keywords: ground-based sky images; multi-step forecasting; convolutional neural networks; image classification; photovoltaic generation (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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