Effective integration of photovoltaic (PV) systems into electric power grids presents significant challenges due to the inherent variability of 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 and 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-based 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. Experimental results show improvements of up to 17.97% on structural similarity and 62.14% on mean squared error, compared to baseline approaches. 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.
Schizas et al. (Tue,) studied this question.
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