A multi-location short-term wind speed prediction model based on spatiotemporal joint learning. (January 2022)

• Record Type: Journal Article
• Authors: Xu, Yuanyuan; Yang, Genke; Luo, Jiliang; He, Jianan; Sun, Haixin
• Is Part Of: Renewable energy. Volume 183(2022)
• Page Start: 148
• Page End: 159
• Publication Date: 2022-01
• Subjects: Multi-location wind speed prediction -- Deep learning -- Spatiotemporal joint learning -- Deformable convolutional network; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.10.075
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Using temporal and spatial correlations to predict wind speed is still one of the most challenging and least-researched fields in wind speed prediction. How to make full use of the spatial correlation of wind speed between adjacent wind turbines to improve the accuracy of wind speed prediction is significant. Therefore, to address the challenges of making multi-location, short-term wind speed predictions, we propose a multi-location wind speed prediction model based on spatiotemporal joint learning, which called Mixed-SqueezeNet-BiGRU. By designing the SqueezeNet model with a deformable convolutional network, the spatial correlation of a multi-location time series is learned by using the spatial wind speed matrix; by introducing the Channel Shuffle operation of ShuffleNet, the characteristics of multi-location and multi-channel are output to ensure the orderly flow of information among the channels. To describe the dynamic information of the time series, a time series dynamic network model based on the bi-directional gating cycle unit, BiGRU, is introduced to model the long-term and nonlinear dependence of the time series. Experiments were carried out on three wind farm's wind speed dataset to verify that the Mixed-SqueezeNet-BiGRU short-term wind speed prediction model has a better prediction performance than some advanced methods. Highlights: Using spatial correlation of wind speed to improve the performance of prediction. Using spatial wind speed matrix as the input of the multi-location prediction model. Obtaining Relative position of each wind turbine by DCN learning position offset. Multi-location wind speed prediction model based on the space-time joint learning.