Abstract：At present, wind power forecasting methods mainly include physical modeling, statistical modeling and artificial intelligence algorithm modeling. The traditional physical modeling and statistical modeling methods are difficult to collect data and select parameters, and have weak processing ability for a large number of data, so it is difficult to establish an accurate prediction model. Therefore, in practical applications, artificial intelligence algorithms are usually used to predict wind power. However, the current research on wind power prediction focuses on the use and improvement of artificial intelligence algorithms, and does not take into account the correlation between different features in the data and wind power, as well as the difference in the size of correlation. The model trained on this basis can only establish a single, superficial correlation, and cannotmine deeper relationships, which is not conducive to short-term prediction of wind power. Therefore, this paper first introduces the feature crossover mechanism, analyzes the correlation of data features and cross combines them, increases feature dimensions, strengthens the learning ability of the algorithm for nonlinear features and deep hidden associations, and forms the FC-CNN-LSTM prediction model based on CNN-LSTM network improvement. Then, use the error value generated by the prediction model in the prediction as the training data, train and generate the error compensation model, and use the data generated by the error compensation model to compensate the wind power prediction data, so as to further improve the prediction accuracy. Finally, through the measured data of a wind farm, it is verified that the FC-CNN-LSTM model has a higher prediction accuracy, and after the error compensation process is added, it can further reduce the error compared with the traditional prediction methods, which has significant advantages. The simulation results of the actual data in a region show that: (1) the feature crossing mechanism can effectively improve the learning ability of the model for nonlinear features and deep hidden associations, thus improving the prediction accuracy of the model. The prediction accuracy of the FC-CNN-LSTM model is 14.3% higher than that of the CNN-LSTM model; (2) The error compensation model based on FC-CNN-LSTM model can accurately predict the error of power prediction model, greatly reducing the prediction error, and greatly improving the prediction accuracy after compensation by 46.5% compared with the FC-CNN-LSTM model before compensation; Finally, the following conclusions are drawn through analysis: (1) Compared with the CNN-LSTM model, the FC-CNN-LSTM model proposed in this paper has obvious advantages in the accuracy of the prediction of the ultra short termminute level wind power. It can more keenly capture the subtle changes of the characteristics related to the wind power, adapt to the rapid changes of meteorological factors, and more accurately predict the wind power, which is more suitable for practical engineering projects; (2) The error compensation mechanism based on FC-CNN-LSTM model proposed in this paper can further improve the accuracy of wind power prediction, and can be used in different application scenarios and with different algorithms, with high adaptability; (3) In addition, the disadvantage of the FC-CNN-LSTM model is that when the wind power is small and close to zero, the wind speed decreases significantly, and the contribution of other weak correlation features in the model will be relatively improved. Although these weak correlation features are related to the wind power to some extent, they do not occupy the same dominant position as the wind speed. Therefore, when the wind speed decreases significantly, the FC-CNN-LSTM model improves the learning of weak correlation features, The prediction accuracy will be worse than that of the CNN-LSTM model, which can be solved by setting a limit on the prediction results according to the wind speed.
刘雨佳, 樊艳芳, 白雪岩, 宋雨露, 郝瑞鑫. 基于特征交叉机制和误差补偿的风力发电功率短期预测[J]. 电工技术学报, 2023, 38(12): 3277-3288.
Liu Yujia, Fan Yanfang, Bai Xueyan, Song Yulu, Hao Ruixin. Short-Term Wind Power Prediction Based on Feature Crossover Mechanism and Error Compensation. Transactions of China Electrotechnical Society, 2023, 38(12): 3277-3288.
[1] 马伟明. 关于电工学科前沿技术发展的若干思考[J]. 电工技术学报, 2021, 36(22): 4627-4636. Ma Weiming.Thoughts on the development of frontier technology in electrical engineering[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4627-4636. [2] 宋琪, 吴可仲. 中国风电迎来“黄金时代”[N]. 中国经营报, 2022-01-03(D07). [3] 李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160. Li Junhui, Zhang Jiahui, Li Cuiping, et al.Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160. [4] 潘超, 李润宇, 蔡国伟, 等. 基于时空关联分解重构的风速超短期预测[J]. 电工技术学报, 2021, 36(22): 4739-4748. Pan Chao, Li Runyu, Cai Guowei, et al.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. [5] 沈小军, 周冲成, 付雪娇. 基于机联网-空间相关性权重的风电机组风速预测研究[J]. 电工技术学报, 2021, 36(9): 1782-1790, 1817. Shen Xiaojun, Zhou Chongcheng, Fu Xuejiao.Wind speed prediction of wind turbine based on the internet of machines and spatial correlation weight[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1782-1790, 1817. [6] Lian Lian, He Kan.Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine[J]. Wind Engineering, 2022, 46(3): 866-885. [7] 李智, 韩学山, 韩力, 等. 地区电网风电场功率超短期预测方法[J]. 电力系统自动化, 2010, 34(7): 90-94. Li Zhi, Han Xueshan, Han Li, et al.An ultra-short-term wind power forecasting method in regional grids[J]. Automation of Electric Power Systems, 2010, 34(7): 90-94. [8] 韩自奋, 景乾明, 张彦凯, 等. 风电预测方法与新趋势综述[J]. 电力系统保护与控制, 2019, 47(24): 178-187. Han Zifen, Jing Qianming, Zhang Yankai, et al.Review of wind power forecasting methods and new trends[J]. Power System Protection and Control, 2019, 47(24): 178-187. [9] 王渝红, 史云翔, 周旭, 等. 基于时间模式注意力机制的BiLSTM多风电机组超短期功率预测[J]. 高电压技术, 2022, 48(5): 1884-1892. Wang Yuhong, Shi Yunxiang, Zhou Xu, et al.Ultra-short-term power prediction for BiLSTM multi wind turbines based on temporal pattern attention[J]. High Voltage Engineering, 2022, 48(5): 1884-1892. [10] Saeed A, Li Chaoshun, Gan Zhenhao, et al.A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution[J]. Energy, 2022, 238: 122012. [11] Gu Bo, Zhang Tianren, Meng Hang, 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. [12] Wang Ruoheng, Li Chaoshun, Fu Wenlong, et al.Deep learning method based on gated recurrent unit and variational mode decomposition for short-term wind power interval prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 3814-3827. [13] 朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802. Zhu Qiaomu, Li Hongyi, Wang Ziqi, et al.Short-term wind power forecasting based on LSTM[J]. Power System Technology, 2017, 41(12): 3797-3802. [14] Shahid F, Zameer A, Muneeb M.A novel genetic LSTM model for wind power forecast[J]. Energy, 2021, 223: 120069. [15] Liu Hui, Chen Dihuang, Lin Fang, et al.Wind power short-term forecasting based on LSTM neural network with dragonfly algorithm[J]. Journal of Physics: Conference Series, 2021, 1748(3): 032015. [16] 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. [17] Wang Kejun, Qi Xiaoxia, Liu Hongda, et al.Deep belief network based k-means cluster approach for short-term wind power forecasting[J]. Energy, 2018, 165: 840-852. [18] 陆悦聪, 王瑞琴, 金楠. 基于多维特征交叉的深度协同过滤算法[J]. 计算机工程与应用, 2022, 58(22): 72-78. Lu Yuecong, Wang Ruiqin, Jin Nan.Deep collaborative filtering algorithm based on multi-dimensional feature crossover[J]. Computer Engineering and Applications: 2022, 58(22): 72-78. [19] 王越, 于莲芝. 一个以注意力机制结合隐式和显式的特征交叉的CTR预估模型[J]. 小型微型计算机系统, 2021, 42(9): 1884-1890. Wang Yue, Yu Lianzhi.CTR prediction model combining implicit and explicit features with attention mechanism[J]. Journal of Chinese Computer Systems, 2021, 42(9): 1884-1890. [20] 张思凡, 牛振东, 陆浩, 等. 基于图卷积嵌入与特征交叉的文献被引量预测方法:以交通运输领域为例[J]. 数据分析与知识发现, 2020, 4(9): 56-67. Zhang Sifan, Niu Zhendong, Lu Hao, et al.Predicting citations based on graph convolution embedding and feature cross: case study of transportation research[J]. Data Analysis and Knowledge Discovery, 2020, 4(9): 56-67. [21] 边春元, 邢海洋, 李晓霞, 等. 基于速度变化率的无位置传感器无刷直流电机风力发电系统换相误差补偿策略[J]. 电工技术学报, 2021, 36(11): 2374-2382. Bian Chunyuan, Xing Haiyang, Li Xiaoxia, et al.Compensation strategy for commutation error of sensorless brushless DC motor wind power generation system based on speed change rate[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2374-2382. [22] 周勇良, 余光正, 刘建锋, 等. 基于改进长期循环卷积神经网络的海上风电功率预测[J]. 电力系统自动化, 2021, 45(3): 183-191. Zhou Yongliang, Yu Guangzheng, Liu Jianfeng, et al.Offshore wind power prediction based on improved long-term recurrent convolutional neural network[J]. Automation of Electric Power Systems, 2021, 45(3): 183-191. [23] Tao Cai, Lu Junjie, Lang Jianxun, et al.Short-term forecasting of photovoltaic power generation based on feature selection and bias compensation-LSTM network[J]. Energies, 2021, 14(11): 3086.
2023, Vol. 38 
