基于支持向量机和误差修正算法的风电短期功率预测
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摘要
基于风电功率预测单一算法带来的预测精度较低问题,提出一种新型的基于粒子群优化支持向量机结合误差修正算法的短期风电功率预测组合算法。该方法首先对原始数据进行分析和清洗;然后通过粒子群算法对支持向量机参数进行寻优,对风电功率进行一次预测,通过经验模态算法对一次预测进行滤波,达到降噪效果,同时得到一次预测误差;最后,利用误差修正算法对一次预测误差进行修正,得到最终的预测值。仿真和测试结果表明,相较于传统的单一算法,该组合算法能更好地提高预测精度。
A new short-term wind power forecast combination algorithm,which is based on particle swarm optimization and support vector machine(SVM),combined with error correction algorithm,has been proposed.Firstly,an analysis and cleaning have been made of the original data;then an optimization can be achieved of the parameters of support vector machine by particle swarm optimization algorithm,followed by a prediction of the wind power.The empirical modal algorithm is used to filter the primary prediction to achieve the effect of noise reduction,thus working out the primary prediction error.Finally,the error correction algorithm is used to correct the one-time prediction error,thus obtaining the final prediction value.The simulation and test results show that the combined algorithm can improve the prediction accuracy better than the traditional single algorithm.
A new short-term wind power forecast combination algorithm,which is based on particle swarm optimization and support vector machine(SVM),combined with error correction algorithm,has been proposed.Firstly,an analysis and cleaning have been made of the original data;then an optimization can be achieved of the parameters of support vector machine by particle swarm optimization algorithm,followed by a prediction of the wind power.The empirical modal algorithm is used to filter the primary prediction to achieve the effect of noise reduction,thus working out the primary prediction error.Finally,the error correction algorithm is used to correct the one-time prediction error,thus obtaining the final prediction value.The simulation and test results show that the combined algorithm can improve the prediction accuracy better than the traditional single algorithm.
引文
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[16]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,29(28):30-33.WANG Lijie,DONG Lei,LIAO Xiaozhong,et al.Short-Term Power Prediction of a Wind Farm Based on Wavelet Analysis[J].Proceedings of the Chinese Society for Electrical Engineering,2009,29(28):30-33.
[17]孟祥星,田成微,冬雷,等.灰色理论用于风力发电容量中长期预测的研究[J].电力系统保护与控制,2011,39(21):81-85.MENG Xiangxing,TIAN Chengwei,DONG Lei,et al.A Study of Grey Theory Used in Prediction of Medium and Long-Term Wind Power Generation[J].Power System Protection and Control,2011,39(21):81-85.
[18]孟岩峰,胡书举,邓雅,等.风电功率预测误差分析及预测误差评价方法[J].电力建设,2013,34(7):6-9.MENG Yanfeng,HU Shuju,DENG Ya,et al.Analysis and Evaluation Method of Wind Power PredictionError[J].Power Construction,2013,34(7):6-9.
[19]LI Kangping,GAO Yajing.A Combination Method for Wind Power Prediction Based on Cooperative Game Theory[J].Electricity,2014(2/3):36-40.
[2]FO CKEN U,LANGE M,HEINEMANN D.Previento:Regional Wind Power Prediction with Risk Control[EB/OL].[2018-05-10].http://www.doc88.com/p-1886563969349.html.
[3]钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016(4):1047-1060.QIAN Zheng,PEI Yan,CAO Lixiao,et al.Review of Wind Power Forecasting Method[J].High Voltage Engineering,2016(4):1047-1060.
[4]朱晓荣,刘艳萍.基于稳健估计时间序列法的风功率预测[J].电力系统及其自动化学报,2012,24(3):107-110,126.ZHU Xiaorong,LIU Yanping.Wind Power Forecasting Using Time Series Model Based on Robust Estimation[J].Proceedings of the CSU-EPSA,2012,24(3):107-110,126.
[5]周松林,茆美琴,苏建徽.基于主成分分析与人工神经网络的风电功率预测[J].电网技术,2011,35(9):128-132.ZHOU Songlin,MAO Meiqin,SU Jianhui.Prediction of Wind Power Based on Principal Component Analysis and Artificial Neural Network[J].Power System Technology,2011,35(9):128-132.
[6]潘迪夫,刘辉,李燕飞.风电场风速短期多步预测改进算法[J].中国电机工程学报,2008,28(26):87-91.PAN Difu,LIU Hui,LI Yanfei.Optimization Algorithm of Short-Term Multi-Step Wind Speed Forecast[J].Proceedings of the Chinese Society for Electrical Engineering,2008,28(26):87-91.
[7]张立影,孟令甲,王泽忠.基于双层BP神经网络的光伏电站输出功率预测[J].电测与仪表,2015,52(11):31-35.ZHANG Liying,MENG Lingjia,WANG Zezhong.Photovoltaic Power Station Output Power Prediction Based on Double BP Neural Network[J].Electrical Measurement and Instrumentation,2015,52(11):31-35.
[8]栗然,柯拥勤,张孝乾,等.基于时序-支持向量机的风电场发电功率预测[J].中国电力,2012,45(1):64-68.LI Ran,KE Yongqin,ZHANG Xiaoqian,et al.Wind Power Forecasting Based on Time Series and SVM[J].Electric Power,2012,45(1):64-68.
[9]丁志勇,杨苹,杨曦,等.基于连续时间段聚类的支持向量机风电功率预测方法[J].电力系统自动化,2012,36(14):131-135,139.DING Zhiyong,YANG Ping,YANG Xi,et al.Wind Power Prediction Method Based on Sequential Time Clustering Support Vector Machine[J].Automation of Electric Power Systems,2012,36(14):131-135,139.
[10]王恺,关少卿,汪令祥,等.基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模[J].电力系统保护与控制,2015,43(2):26-32.WANG Kai,GUAN Shaoqing,WANG Lingxiang,et al.A Combined Forecasting Model for Wind Power Prediction Based on Fuzzy Information Granulation and Least Squares Support Vector Machine[J].Power System Protection and Control,2015,43(2):26-32.
[11]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):64-69.LI Xiao,WANG Xin,ZHENG Yihui,et al.ShortTerm Wind Load Forecasting Based on Improved LSSVMand Error Forecasting Correction[J].Power System Protection and Control,2015,43(11):64-69.
[12]陈道君,龚庆武,金朝意,等.基于自适应扰动量子粒子群算法参数优化的支持向量回归机短期风电功率预测[J].电网技术,2013,37(4):974-980.CHEN Daojun,GONG Qingwu,JIN Zhaoyi,et al.Short-Term Wind Power Prediction Based on Support Vector Regression Machine Optimized by Adaptive Disturbance Quantum-Behaved Particle Swarm Optimization[J].Power System Technology,2013,37(4):974-980.
[13]杨志凌,刘永前.应用粒子群优化算法的短期风电功率预测[J].电网技术,2011,35(5):159-164.YANG Zhiling,LIU Yongqian.Short-Term Wind Power Prediction with Particle Swarm Optimization[J].Power System Technology,2011,35(5):159-164.
[14]王永翔,陈国初.基于改进鱼群优化支持向量机的短期风电功率预测[J].电测与仪表,2016,53(3):80-84.WANG Yongxiang,CHEN Guochu.Short-Term Wind Power Prediction Based on IAFSA Optimizatin SVM[J].Electrical Measurement and Instrumentation,2016,53(3):80-84.
[15]叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型[J].中国电机工程学报,2011,31(31):102-108.YE Lin,LIU Peng.Combined Model Based on EMD-SVM for Short-Term Wind Power Prediction[J].Proceedings of the Chinese Society for Electrical Engineering,2011,31(31):102-108.
[16]王丽婕,冬雷,廖晓钟,等.基于小波分析的风电场短期发电功率预测[J].中国电机工程学报,2009,29(28):30-33.WANG Lijie,DONG Lei,LIAO Xiaozhong,et al.Short-Term Power Prediction of a Wind Farm Based on Wavelet Analysis[J].Proceedings of the Chinese Society for Electrical Engineering,2009,29(28):30-33.
[17]孟祥星,田成微,冬雷,等.灰色理论用于风力发电容量中长期预测的研究[J].电力系统保护与控制,2011,39(21):81-85.MENG Xiangxing,TIAN Chengwei,DONG Lei,et al.A Study of Grey Theory Used in Prediction of Medium and Long-Term Wind Power Generation[J].Power System Protection and Control,2011,39(21):81-85.
[18]孟岩峰,胡书举,邓雅,等.风电功率预测误差分析及预测误差评价方法[J].电力建设,2013,34(7):6-9.MENG Yanfeng,HU Shuju,DENG Ya,et al.Analysis and Evaluation Method of Wind Power PredictionError[J].Power Construction,2013,34(7):6-9.
[19]LI Kangping,GAO Yajing.A Combination Method for Wind Power Prediction Based on Cooperative Game Theory[J].Electricity,2014(2/3):36-40.