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The method leverages fisheye camera-captured sky images to extract spatiotemporal features via a three-dimensional convolutional neural network (3DCNN), and integrates a lightweight time-series model, DLinear, to enable efficient prediction. . However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor. . To address this issue, this paper proposes a novel short-term PV power prediction approach based on low-cost ground-based sky image sequences: the 3DCNN-DLinear model.
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