A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster

Mumin Zhang, Yuzhi Wang, Haochen Zhang, Zhiyun Peng and Junjie Tang ()
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Mumin Zhang: University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400044, China
Yuzhi Wang: University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400044, China
Haochen Zhang: Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
Zhiyun Peng: School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Junjie Tang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China

Mathematics, 2023, vol. 11, issue 3, 1-17

Abstract: Wind energy has been widely used in recent decades to achieve green and sustainable development. However, wind speed prediction in wind farm clusters remains one of the less studied areas. Spatial features of cluster data of wind speed are not fully exploited in existing work. In addition, missing data, which dramatically deteriorate the forecasting performance, have not been addressed thoroughly. To tackle these tough issues, a new method, termed input set based on wind farm cluster data–deep extreme learning machine (IWC-DELM), is developed herein. This model builds an input set based on IWC, which takes advantage of the historical data of relevant wind farms to utilize the spatial characteristics of wind speed sequences within such wind farm clusters. Finally, wind speed prediction is obtained after the training of DELM, which results in a better performance in forecasting accuracy and training speed. The structure IWC, complete with the multidimensional average method (MDAM), is also beneficial to make up the missing data, thus enhancing data robustness in comparison to the traditional method of the moving average approach (MAA). Experiments are conducted with some real-world data, and the results of gate recurrent unit (GRU), long- and short-term memory (LSTM) and sliced recurrent neural networks (SRNNs) are also taken for comparison. These comparative tests clearly verify the superiority of IWC-DELM, whose accuracy and efficiency both rank at the top among the four candidates.

Keywords: wind speed forecasting; wind farm cluster; input set based on wind farm cluster data; robustness analysis; deep extreme learning machine; multidimensional average method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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