Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Yuwei Fu (Autor) Outra; Hua Chai (Autor) Outra; Zhao Zhen (Autor) Outra; Fei Wang (Autor) Outra; Xunjian Xu (Autor) Outra; Kangping Li (Autor) Outra; Miadreza Shafie-Khah (Autor) Outra; Payman Dehghanian (Autor) Outra; João P. S. Catalão (Autor) FEUP

Revista

Título: IEEE Transactions on Industry Applications

Vol. 57

Páginas: 3272-3281

ISSN: 0093-9994

Editora: IEEE

Indexação

ISI Web of Knowledge - 20 Citações

Scopus - 28 Citações

Outras Informações

ID Authenticus: P-00V-1AP

DOI: 10.1109/tia.2021.3072025

Abstract (EN): 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 autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10