Abstract
Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch of the power system, but it faces two challenges: (1) The dimensions of the input parameters for WPP of wind farm clusters are very high so that the input parameters contain irrelevant or redundant features; (2) it is difficult to build a holistic WPP model with high-dimensional input parameters for wind farm clusters. To overcome these challenges, a novel short-term WPP model for wind farm clusters, based on sequential floating forward selection (SFFS) feature selection and bidirectional long short-term memory (BLSTM) deep learning, is proposed in this paper. First, more than 300,000 input features of the wind farm cluster are constructed. Second, the SFFS method is applied to sort the high-dimensional features and analyze the rule that the forecasting accuracy changes with the number of features to obtain the optimal number of features and feature sets. Finally, based on the results of feature selection, BLSTM is applied to build a WPP model for wind farm clusters with a combination of feature selection and deep learning. This case study shows that (1) SFFS is an effective method for selecting the core features for WPP of wind farm clusters; (2) BLSTM shows not only higher WPP accuracy than long short-term memory and backpropagation neural network but also outstanding performance in terms of reducing the phase errors of WPP.
Highlights
According to the statistical results from the Global Wind Energy Council (GWEC), at the end of 2019, the total installed capacity of global wind farms reached 651 GW [1]
Based on the results of feature selection, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM) are comparatively applied to carry out Wind power prediction (WPP) for wind farm clusters
L is set to be the difference between the number of target features d and the number of selected features n multiplied by a coefficient, and the coefficient is recommended to be 10%, that is, L = (d − n) × 10% [36]
Summary
According to the statistical results from the Global Wind Energy Council (GWEC), at the end of 2019, the total installed capacity of global wind farms reached 651 GW [1]. (2) Based on the results of feature selection, a short-term WPP model for wind farm clusters, named SFFS-BLSTM, combining SFFS feature selection and BLSTM deep learning, is proposed in the paper, which shows excellent characteristics of reducing prediction errors, especially phase errors. Based on the results of optimal feature selection, statistical analysis is applied to obtain the most important factors affecting the WPP accuracy of wind farm clusters. Based on the results of feature selection, LSTM and BLSTM are comparatively applied to carry out WPP for wind farm clusters. Based on the WPP results obtained ionf 1s8tep 8, the root mean square error (RMSE) of the WPPs and wind power outputs of the WPPs for LSTM and BLSTM are comparatively analyzed to assess the two methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
