A novel deep learning ensemble model with data denoising for short-term wind speed forecasting. (1st March 2020)

• Record Type: Journal Article
• Authors: Peng, Zhiyun; Peng, Sui; Fu, Lidan; Lu, Binchun; Tang, Junjie; Wang, Ke; Li, Wenyuan
• Is Part Of: Energy conversion and management. Volume 207(2020)
• Publication Date: 2020-03-01
• Subjects: Wind speed forecasting -- Deep learning -- Wavelet soft threshold denoising -- Gated recurrent unit -- Time series forecasting; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.112524
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: The wavelet soft threshold denoising (WSTD) is systematically introduced. The problem that is hardly tackled by decomposition methods is solved via the WSTD. A deep learning method gated recurrent unit (GRU) is successfully developed. The WSTD-GRU has a good performance in accuracy, speed, volatility and adaptability. The parameters of the proposed model are adjusted by cross-validated grid-search. Abstract: Wind speed forecasting plays a pivotal role in the security and economy of the power system operation. However, accurate prediction on the wind speed value is still quite challenging due to three features of wind speed: randomness, fluctuation and unpredictability. Though many previous works have studied decomposition-based methods for preprocessing, these methods are difficult to use in actual prediction, since the newly acquired data will greatly affect the values of the initial decomposed subsequences. To solve this problem, a good solution referred as the wavelet soft threshold denoising (WSTD) is proposed herein, which is also systematically introduced for the first time. Moreover, a novel deep learning method gated recurrent unit (GRU) is successfully developed to be combined with WSTD to forecast the wind speed series in this paper. In the ensemble WSTD-GRU model, the WSTD is applied to filter the redundant information from the original data of wind speed series; GRU, which is particularly advantageous for learning the features in case of cooperating with WSTD, is adopted to predict the future multi-step wind speed values. Also, the parameters of the GRU are adjusted by cross-validated grid-search, and they are visualized in the paper. In experiments, thirty-five commonly used models are involved for comparison to evaluate the forecasting performance of the proposed model, through the datasets from four sites in the United States covering a wide range of longitudes. Finally, a superior performance of this methodology with relatively high accuracy, fast forecasting speed, small volatility of results and good adaptability is verified by the test results of case studies aforementioned.