引用本文:李霄,王昕,郑益慧,等.基于改进最小二乘支持向量机和预测误差校正的 短期风电负荷预测[J].电力系统保护与控制,2015,43(11):63-69.
LI Xiao,WANG Xin,ZHENG Yihui,et al.Short-term wind load forecasting based on improved LSSVM and error forecasting correction[J].Power System Protection and Control,2015,43(11):63-69
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基于改进最小二乘支持向量机和预测误差校正的 短期风电负荷预测
李霄1, 王昕1, 郑益慧1, 李立学1, 生西奎2, 吴昊2
1.上海交通大学电工与电子技术中心，上海 200240;2.国网吉林省电力有限公司 延边供电公司，吉林 延边 133000
摘要:
为了提高风电负荷预测精度，保证风电场资源得到有效利用，提出了基于改进最小二乘支持向量机和预测误差校正相结合的方法。首先引入提升小波分解原始数据，可以有效提取其主要特征，从而克服风电场的随机性。然后采用最小二乘支持向量机对分解后的信号做预测，保证了预测精度。接着用误差校正方式修正预测结果，减少了较大误差点的出现，提高了预测结果的稳定性。最后，通过某风电场预测结果表明，基于提升小波和最小二乘支持向量机的方法可以提高预测的精度，误差预测的方法也可以有效地校正预测结果。仿真结果验证了该方法用于风电负荷预测是有效可行的。
关键词:  提升小波  最小二乘支持向量机  误差预测  风电负荷预测
DOI：10.7667/j.issn.1674-3415.2015.11.010
分类号:
基金项目:国家自然科学基金(60504010)；国家高新技术863发展计划(2008AA04Z129)；上海市自然科学基金(14ZR1421800)；流程工业综合自动化国家重点实验室开放课题基金资助
Short-term wind load forecasting based on improved LSSVM and error forecasting correction
LI Xiao1, WANG Xin1, ZHENG Yihui1, LI Lixue1, SHENG Xikui2, WU Hao2
1.Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China;2.Yanbian Power Supply Company, Jilin Electric Power Co., Ltd., State Grid Corporation of China, Yanbian 133000, China
Abstract:
In order to improve the wind load forecasting precision and ensure the effective use of wind power resources, a method based on improved least square support vector machine (LSSVM) combined with error forecasting is proposed. Firstly, lifting wavelet transform (LWT) decomposition of the original data is introduced to effectively extract the main features, with which the randomness of the wind is overcome; secondly LSSVM is used for the prediction of decomposed signals to ensure accuracy; then, error forecasting (EF) is added to reduce the large error points and improve the stability of the results. Finally, experimental results using real wind farm data show that the forecasting model is better in both generalization performance and predictive accuracy, and may provide an effective and practical way for the short-term wind load forecasting.
Key words:  lifting wavelet transform (LWT)  least square support vector machine (LSSVM)  error forecasting (EF)  wind load forecasting
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