Ultra-short-term probabilistic wind power forecasting with spatial-temporal multi-scale features and K-FSDW based weight. (1st February 2023)

• Record Type: Journal Article
• Authors: Che, Jinxing; Yuan, Fang; Deng, Dewen; Jiang, Zheyong
• Is Part Of: Applied energy. Volume 331(2023)
• Publication Date: 2023-02-01
• Subjects: Probabilistic wind power forecasting -- Spatial-temporal multi-scale features -- Dynamic weighting -- Kernel density estimation -- Quantile forecasting; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120479
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Features are constructed in both space and time, and spatial-temporal multi-scale feature selection is performed on them. A dynamic sparse weighting algorithm based on K-Forward nearest neighbours is proposed to combine individual quantile models. The probability density function provides uncertainty in the wind speed and the results confirm the validity of the model. Abstract: As a potential cleaner energy technology, wind power is a pollution-free and inexhaustible energy, which make a significant contribution to the global energy transformation. Most studies have focused on the accurate forecasting to help the management of the wind power grid-tied. Considering the need for the quantitative modeling of the endogenous random fluctuations and uncertainties involved, a novel ultra-short-term probabilistic wind power forecasting with spatial–temporal multi-scale features and K-FSDW based weight is proposed, which includes data decomposition, multi-scale feature selection, individual model training, dynamic weighting using K-FSDW, error correction modeling by RF and probability density forecasting by KDE, to create uncertainty quantification for power grid dispatching and operation. Firstly, the normalized target wind speed is decomposed into multiple subsequences through VMD, the subsequence and adjacent spatial wind speed series are reconstructed into spatiotemporal candidate features, and spatial–temporal multi-scale feature selection is carried out. Secondly, different quantile regression models are used to predict each subsequence, and a quantile dynamic sparse weighted combination algorithm based on K-forward nearest neighbor is proposed to combine the prediction results of each model, and then reorganize the prediction results of subsequences. Finally, the RF model is used for error correction, and the probability density function is obtained by kernel density estimation. In the experimental comparison and analysis, taking the actual data of an offshore wind farm in Penglai District, Shandong Province, China as an example, the feasibility and effectiveness of the model are verified.