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The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution
information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has
been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal  information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional auto- encoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of 2D CAEs and 3D CAEs. For 2D CAEs and 3D CAEs, 4 architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry (PIV) and Fourier phase correlation theory (FPCT) are introduced to build the benchmark models. Besides, 5 different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.

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