Multi-Step Ultra-Short-Term Wind Speed Prediction Based on Decomposition and Reconstruction of Time-Spatial Correlation
Pan Chao1, Li Runyu1, Cai Guowei1, Wang Dian1, Zhang Yonghui2
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China; 2. Songhuajiang Hydropower Co. Ltd Jilin Baishan Power Plant Jilin 132400 China
Abstract Considering the spatial correlation of wind speed makes multi-step prediction,which is a research hotspot of large-scale wind power grid integration. This paper adopts an improved multi-position multi-step wind speed prediction method. First, a decomposition and reconstruction strategy of wind speed matrix time-spatial correlation is proposed, and the gray correlation analysis is performed on the wind turbines in the wind farm. Based on this, the improved spatial association algorithm is used to optimize and sort, and obtain spatial information of typical wind turbine and neighboring domains. Information is reconstructed to improve the efficiency of spatial feature extraction. Then, the reconstructed spatio-temporal three-dimensional information is input into the convolutional memory network to reduce the impact of the lack of information on the prediction accuracy, and spatial feature extraction and multi-step ultra-short-term prediction are performed. Finally, the proposed method verify the prediction accuracy and generalization ability by predicting the wind speed and wind power of different wind farms.
Pan Chao,Li Runyu,Cai Guowei等. Multi-Step Ultra-Short-Term Wind Speed Prediction Based on Decomposition and Reconstruction of Time-Spatial Correlation[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4739-4748.
[1] 孙国强, 卫志农, 翟玮星. 基于RVM与ARMA误差校正的短期风速预测[J]. 电工技术学报, 2012, 27(8): 187-193. Sun Guoqiang, Wei Zhinong, Zhai Weixing.Short term wind speed forecasting based on RVM and ARMA error correcting[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 187-193. [2] 王典, 潘超, 鹿丽, 等. 计及风-光时序相关特性的源-储并网阶段式规划策略[J]. 东北电力大学学报, 2020, 40(04): 1-10. Wang Dian, Pan Chao, Lu Li, et al.Source-storage staged planning strategy considering wind- photovoltaic timing related characteristics[J]. Journal of Northeast Electric Power University, 2020, 40(04): 1-10. [3] 殷豪, 欧祖宏, 陈德, 等. 基于二次模式分解和级联式深度学习的超短期风电功率预测[J]. 电网技术, 2020, 44(2): 445-453. Yin Hao, Ou Zuhong, Chen De, et al.Ultra-short- term wind power prediction based on two-layer mode decomposition and cascaded deep learning[J]. Power System Technology, 2020, 44(2):445-453. [4] 冯双磊, 王伟胜, 刘纯, 等. 基于物理原理的风电场短期风速预测研究[J]. 太阳能学报, 2011, 32(5): 611-616. Feng Shuanglei, Wang Weisheng, Liu Chun, et al.Short term wind speed prediction based on physical principle[J]. Acta Energiae Solaris Sinica, 2011, 32(5): 611-616. [5] 夏冬, 吴俊勇, 贺电, 等.一种新型的风电功率预测综合模型[J]. 电工技术学报, 2011, 26(增刊1): 262-266. Xia Dong, Wu Junyong, He Dian, et al.A novel combined model for wind power forecasting based on maximum entropy principle[J]. Transactions of China Electrotechnical Society, 2011, 26(S1): 262-266. [6] 田中大, 李树江, 王艳红, 等. 基于小波变换的风电场短期风速组合预测[J]. 电工技术学报, 2015, 30(9): 112-120. Tian Zhongda, Li Shujiang, Wang Yanhong, et al.Short-term wind speed combined prediction for wind farms based on wavelet transform[J]. Transactions of China Electrotechnical Society, 2015, 30(9): 112-120. [7] 范静仪, 施建强, 惠子轩. 基于BP神经网络的风电功率预测研究[J]. 电工技术, 2020(11): 23-25, 29. Fan Jingyi, Shi Jianqiang, Hui Zixuan.Wind power prediction based on BP neural network[J]. Electrical Engineering, 2020(11): 23-25, 29. [8] 王贺, 胡志坚, 张翌晖, 等. 基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J]. 电工技术学报, 2014, 29(4): 237-245. Wang He, Hu Zhijian, Zhang Yihui, et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 237-245. [9] 向玲, 邓泽奇, 赵玥. 基于LPF-VMD和KELM的风速多步预测模型[J]. 电网技术, 2019, 43(12): 4461-4467. Xiang Ling, Deng Zeqi, Zhao Yue.Multi-step wind speed prediction model based on LPF-VMD and KELM[J]. Power System Technology, 2019, 43(12):4461-4467. [10] Bo Gu, Tianren Zhang, Hang Meng, et al.Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation[J]. Renewable Energy, 2021, 164: 687-708. [11] 杨茂, 宾阳, 江博. 基于概率分布量化指标和灰色关联决策的风电功率实时预测研究[J]. 中国电机工程学报, 2017, 37(24): 7099-7107, 7424. Yang Mao, Huang Binyang, Jiang Bo.Real-time wind power prediction based on probability distribution and gray relational decision-making[J]. Proceedings of the CSEE, 2017, 37(24): 7099-7107, 7424. [12] Wang Huaizhi, Li Gangqiang, Wang Guibin, et al.Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017, 188: 56-70. [13] 陈金富, 朱乔木, 石东源, 等. 利用时空相关性的多位置多步风速预测模型[J]. 中国电机工程学报, 2019, 39(7): 2093-2106. Chen Jinfu, Zhu Qiaomu, Shi Dongyuan, et al.Multi-position multi-step wind speed prediction model using space-time correlation[J]. Proceedings of the CSEE, 2019, 39(7): 2093-2106. [14] Zhang Zhenzhen, Zou Jianxiao, Zheng Gang, Ultra-short term wind power prediction model based on modified grey model method for power control in wind farm[J]. Wind Engineering, 2011, 35: 55-68. [15] Sun Mucun, Feng Cong, Zhang Jie.Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation[J]. Applied Energy, 2019, 256: 113842 [16] Asadi R, Regan A C.A spatio-temporal decomposition based deep neural network for time series forecasting[J]. Applied Soft Computing, 2019, 87: 105963. [17] 王晨, 寇鹏. 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测[J]. 电工技术学报, 2020, 35(13): 2723-2735. Wang Chen, Kou Peng.Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735. [18] Yang Mao, Zhang Luobin, Cui Yang, et al.The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors[J]. Journal of Renewable and Sustainable Energy, 2019, 11(3): 033304. [19] 潘超, 秦本双. 基于实测数据的大型风电场功率模型研究[J]. 电网技术, 2017, 41(8): 2625-2631. Pan Chao, Qin Benshuang.Research on power model of large-scale wind farm based on measured data[J]. Power System Technology, 2017, 41(8): 2625-2631.
2021, Vol. 36 
