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

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Chai, H (Author) Other; Zhen, Z (Author) Other; Li, KP (Author) Other; Wang, F (Author) Other; Dehghanian, P (Author) Other; Shafie khah, M (Author) Other; Catalao, JPS (Author) FEUP

Conference proceedings International

Title: IEEE Industry Applications Society Annual Meeting, IAS 2020, Detroit, MI, USA, October 10-16, 2020

Pages: 1-7

2020 IEEE Industry Applications Society Annual Meeting, IAS 2020

10 October 2020 through 16 October 2020

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00T-GVA

DOI: 10.1109/ias44978.2020.9334923

Abstract (EN): The precise minute time scale forecasting of an individual Photovoltaic power station output relies on accurate sky image prediction. To avoid the two deficiencies of traditional digital image processing technology (DIPT) in predicting sky images: relatively limited input spatiotemporal information and linear extrapolation of images, convolutional auto-encoder (CAE) based sky image prediction models are proposed according to the spatiotemporal feature extraction ability of 2D and 3D convolutional layers. 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. The results show that the proposed models outperform the benchmark models under different scenarios.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7