基于智能相似日识别及偏差校正的短期负荷预测方法
|
摘要
在传统负荷预测理论的基础上,提出了基于智能相似日识别及偏差校正的新型短期负荷预测方法。首先构建地市—相关因素特征矩阵,通过判断矩阵相关性智能选取负荷相似日,从而实现负荷曲线的一次预测。在此基础上,建立了实时气象偏差校正策略,采用XGBoost算法进行负荷曲线的二次偏差校正,达到短期负荷预测的目标。算例研究表明,该策略能够有效提升短期负荷预测精度,而且具有较好的自适应特性,可以应用于电力系统短期负荷预测实践。
Based on the traditional load forecasting theory, this paper proposes a new short-term load forecasting method based on intelligent similar day recognition and deviation correction. Firstly, the characteristic matrix of prefecture-city and correlation factors is constructed to select the most similar day of load curve through calculating matrix correlation coefficient. On this basis, the real-time meteorological deviation correction strategy which adopts the XGBoost algorithm is established to carry out the secondary deviation correction of the load curve, so as to achieve the goal of short-term load prediction. An example study shows that this strategy can effectively improve accuracy of short-term load forecasting, and also has good adaptive characteristics. Therefore, this method can be applied to the short-term power load forecasting practice.
Based on the traditional load forecasting theory, this paper proposes a new short-term load forecasting method based on intelligent similar day recognition and deviation correction. Firstly, the characteristic matrix of prefecture-city and correlation factors is constructed to select the most similar day of load curve through calculating matrix correlation coefficient. On this basis, the real-time meteorological deviation correction strategy which adopts the XGBoost algorithm is established to carry out the secondary deviation correction of the load curve, so as to achieve the goal of short-term load prediction. An example study shows that this strategy can effectively improve accuracy of short-term load forecasting, and also has good adaptive characteristics. Therefore, this method can be applied to the short-term power load forecasting practice.
引文
[1]康重庆,夏清,张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004,28(17):1-11.KANG Chongqing,XIA Qing,ZHANG Boming.Review of power system load forecasting and its development[J].Automation of Electric Power Systems,2004,28(17):1-11.
[2]廖旎焕,胡智宏,马莹莹,等.电力系统短期负荷预测方法综述[J].电力系统保护与控制,2011,39(1):147-152.LIAO Nihuan,HU Zhihong,MA Yingying,et al.Review of the short-term load forecasting methods of electric power system[J].Power System Protection and Control,2011,39(1):147-152.
[3]黎灿兵,李晓辉,赵瑞,等.电力短期负荷预测相似日选取算法[J].电力系统自动化,2008,32(9):69-73.LI Canbing,LI Xiaohui,ZHAO Rui,et al.A novel algorithm of selecting similar days for short-term power load forecasting[J].Automation of Electric Power Systems,2008,32(9):69-73.
[4]郭华安,加玛力汗·库马什,常喜强,等.电力系统短期负荷预测方法与预测精度综述[J].电气技术,2011(10):20-23.GUO Huaan,Jamalihan·Kumashi,CHANG Xiqiang,et al.Review of the short-term load forecasting methods and the accurate prediction of power system[J].Electric Technologies,2011(10):20-23.
[5]王惠中,周佳,刘轲.电力系统短期负荷预测方法研究综述[J].电气自动化,2015,37(1):1-3.WANG Huizhong,ZHOU Jia,LIU Ke.Summary of research on the short-term load forecasting method of the electric power system[J].Electrical Automation,2015,37(1):1-3.
[6]康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.
[7]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14):26-32.HUANG Yuansheng,DENG Jiajia,YUAN Zhenzhen.SVM short-term load forecasting based on ARMA error calibration and the adaptive particle swarm optimization[J].Power System Protection and Control,2011,39(14):26-32.
[8]李军,於阳.基于稀疏编码的短期风电功率时间序列预测[J].电力系统保护与控制,2018,46(12):16-23.LI Jun,YU Yang.Short-term wind power time series prediction based on sparse coding method[J].Power System Protection and Control,2018,46(12):16-23.
[9]张振高,杨正瓴.短期负荷预测中的负荷求导法及天因素的使用[J].电力系统及其自动化学报,2006,18(5):79-83.ZHANG Zhengao,YANG Zhengling.Load derivation in short term forecasting using weather factor[J].Proceedings of the CSU-EPSA,2006,18(5):79-83.
[10]靳春旭,董福贵.长期风电负荷预测方法比较[J].广东电力,2018,31(9):70-76.JIN Chunxu,DONG Fugui.Comparison of prediction methods for long-term wind power load[J].Guangdong Electric Power,2018,31(9):70-76.
[11]李辉.改进残差GM(1,1)模型在中长期负荷预测中的应用[J].广东电力,2017,30(9):81-85.LI Hui.Application of improved residual error GM(1,1)model in medium and long term load forecasting[J].Guangdong Electric Power,2017,30(9):81-85.
[12]ZHENG Dehua,ESEYE A T,ZHANG Jianhua,et al.Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids[J].Protection and Control of Modern Power Systems,2017,2(2):136-145.DOI:10.1186/s41601-017-0041-5.
[13]谷云东,张素杰,冯君淑.大用户电力负荷的多模型模糊综合预测[J].电工技术学报,2015,30(23):110-115.GU Yundong,ZHANG Sujie,FENG Junshu.Multi-model fuzzy synthesis forecasting of electric power loads for larger consumers[J].Transactions of China Electrotechnical Society,2015,30(23):110-115.
[14]张平,潘学萍,薛文超.基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J].电力自动化设备,2012,32(11):121-125.ZHANG Ping,PAN Xueping,XUE Wenchao.Short-term load forecasting based on wavelet decomposition,fuzzy gray correlation clustering and BP neural network[J].Electric Power Automation Equipment,2012,32(11):121-125.
[15]刘会家,管鑫,陈波,等.考虑主动需求的主动配电网负荷预测[J].电力系统保护与控制,2018,46(10):68-74.LIU Huijia,GUAN Xin,CHEN Bo,et al.Load forecasting for active distribution network in the presence of active demand[J].Power System Protection and Control,2018,46(10):68-74.
[16]黄晗,孙堃,刘达.基于随机森林的电力系统小时负荷预测研究[J].智慧电力,2018,46(5):8-14.HUANG Han,SUN Kun,LIU Da.Hourly load forecasting of power system based on random forest[J].Smart Power,2018,46(5):8-14.
[17]王志勇,郭创新,曹一家.基于模糊粗糙集和神经网络的短期负荷预测方法[J].中国电机工程学报,2005,25(19):7-11.WANG Zhiyong,GUO Chuangxin,CAO Yijia.A method for short term load forecasting integrating fuzzy-rough set with artificial neural network[J].Proceedings of the CSEE,2005,25(19):7-11.
[18]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.WU Xiaoyu,HE Jinghan,ZHANG Pei,et al.Power system short-term load forecasting based on improved random forest with gray relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55.
[19]林芳,林焱,吕宪龙,等.基于均衡KNN算法的电力负荷短期并行预测[J].中国电力,2018,51(10):88-94.LIN Fang,LIN Yan,LüXianlong,et al.Short-term parallel power load forecasting based on balanced KNN[J].Electric Power,2018,51(10):88-94.
[20]赵芝璞,高超,沈艳霞,等.基于关联模糊神经网络和改进型蜂群算法的负荷预测方法[J].中国电力,2018,51(2):54-60.ZHAO Zhipu,GAO Chao,SHEN Yanxia,et al.A method for load forecasting based on correlated fuzzy neural network and improved artificial bee colony algorithm[J].Electric Power,2018,51(2):54-60.
[21]张炀,汪洋,祝宇翔,等.基于PAM和ELM的电力短期负荷预测相似日选取算法[J].电力大数据,2017,20(12):84-87.ZHANG Yang,WANG Yang,ZHU Yuxiang,et al.Similarity day selection algorithm for power short-term load forecasting[J].Power Systems and Big Data,2017,20(12):84-87.
[22]张冰玉.基于数据挖掘技术的短期电力负荷预测[J].电力大数据,2017,20(10):18-21.ZHANG Bingyu.Prediction of short-term power load based on data mining technology[J].Power Systems and Big Data,2017,20(10):18-21.
[23]曲正伟,张坤,王云静,等.基于PSO-OMP优化的WD-ASD超短期负荷预测[J].电工电能新技术,2017,36(12):39-45.QU Zhengwei,ZHANG Kun,WANG Yunjing,et al.Short-term load forecasting based on WD-ASD optimized by PSO-OMP[J].Advanced Technology of Electrical Engineering and Energy,2017,36(12):39-45.
[24]李啸骢,李春涛,从兰美,等.基于动态权值相似日选取算法的短期负荷预测[J].电力系统保护与控制,2017,45(6):1-8.LI Xiaocong,LI Chuntao,CONG Lanmei,et al.Shortterm load forecasting based on dynamic weight similar day selection algorithm[J].Power System Protection and Control,2017,45(6):1-8.
[25]SENJYU T,TAKARA H,UEZATO K,et al.One-hour-ahead load forecasting using neural network[J].IEEE Transactions on Power Systems,2002,17(1):113-118.
[26]孙谦,姚建刚,赵俊,等.基于最优交集相似日选取的短期母线负荷综合预测[J].中国电机工程学报,2013,33(4):126-134.SUN Qian,YAO Jiangang,ZHAO Jun,et al.Short-term bus load integrated forecasting based on selecting optimal intersection similar days[J].Proceedings of the CSEE,2013,33(4):126-134.
[2]廖旎焕,胡智宏,马莹莹,等.电力系统短期负荷预测方法综述[J].电力系统保护与控制,2011,39(1):147-152.LIAO Nihuan,HU Zhihong,MA Yingying,et al.Review of the short-term load forecasting methods of electric power system[J].Power System Protection and Control,2011,39(1):147-152.
[3]黎灿兵,李晓辉,赵瑞,等.电力短期负荷预测相似日选取算法[J].电力系统自动化,2008,32(9):69-73.LI Canbing,LI Xiaohui,ZHAO Rui,et al.A novel algorithm of selecting similar days for short-term power load forecasting[J].Automation of Electric Power Systems,2008,32(9):69-73.
[4]郭华安,加玛力汗·库马什,常喜强,等.电力系统短期负荷预测方法与预测精度综述[J].电气技术,2011(10):20-23.GUO Huaan,Jamalihan·Kumashi,CHANG Xiqiang,et al.Review of the short-term load forecasting methods and the accurate prediction of power system[J].Electric Technologies,2011(10):20-23.
[5]王惠中,周佳,刘轲.电力系统短期负荷预测方法研究综述[J].电气自动化,2015,37(1):1-3.WANG Huizhong,ZHOU Jia,LIU Ke.Summary of research on the short-term load forecasting method of the electric power system[J].Electrical Automation,2015,37(1):1-3.
[6]康重庆,夏清,刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.
[7]黄元生,邓佳佳,苑珍珍.基于ARMA误差修正和自适应粒子群优化的SVM短期负荷预测[J].电力系统保护与控制,2011,39(14):26-32.HUANG Yuansheng,DENG Jiajia,YUAN Zhenzhen.SVM short-term load forecasting based on ARMA error calibration and the adaptive particle swarm optimization[J].Power System Protection and Control,2011,39(14):26-32.
[8]李军,於阳.基于稀疏编码的短期风电功率时间序列预测[J].电力系统保护与控制,2018,46(12):16-23.LI Jun,YU Yang.Short-term wind power time series prediction based on sparse coding method[J].Power System Protection and Control,2018,46(12):16-23.
[9]张振高,杨正瓴.短期负荷预测中的负荷求导法及天因素的使用[J].电力系统及其自动化学报,2006,18(5):79-83.ZHANG Zhengao,YANG Zhengling.Load derivation in short term forecasting using weather factor[J].Proceedings of the CSU-EPSA,2006,18(5):79-83.
[10]靳春旭,董福贵.长期风电负荷预测方法比较[J].广东电力,2018,31(9):70-76.JIN Chunxu,DONG Fugui.Comparison of prediction methods for long-term wind power load[J].Guangdong Electric Power,2018,31(9):70-76.
[11]李辉.改进残差GM(1,1)模型在中长期负荷预测中的应用[J].广东电力,2017,30(9):81-85.LI Hui.Application of improved residual error GM(1,1)model in medium and long term load forecasting[J].Guangdong Electric Power,2017,30(9):81-85.
[12]ZHENG Dehua,ESEYE A T,ZHANG Jianhua,et al.Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids[J].Protection and Control of Modern Power Systems,2017,2(2):136-145.DOI:10.1186/s41601-017-0041-5.
[13]谷云东,张素杰,冯君淑.大用户电力负荷的多模型模糊综合预测[J].电工技术学报,2015,30(23):110-115.GU Yundong,ZHANG Sujie,FENG Junshu.Multi-model fuzzy synthesis forecasting of electric power loads for larger consumers[J].Transactions of China Electrotechnical Society,2015,30(23):110-115.
[14]张平,潘学萍,薛文超.基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J].电力自动化设备,2012,32(11):121-125.ZHANG Ping,PAN Xueping,XUE Wenchao.Short-term load forecasting based on wavelet decomposition,fuzzy gray correlation clustering and BP neural network[J].Electric Power Automation Equipment,2012,32(11):121-125.
[15]刘会家,管鑫,陈波,等.考虑主动需求的主动配电网负荷预测[J].电力系统保护与控制,2018,46(10):68-74.LIU Huijia,GUAN Xin,CHEN Bo,et al.Load forecasting for active distribution network in the presence of active demand[J].Power System Protection and Control,2018,46(10):68-74.
[16]黄晗,孙堃,刘达.基于随机森林的电力系统小时负荷预测研究[J].智慧电力,2018,46(5):8-14.HUANG Han,SUN Kun,LIU Da.Hourly load forecasting of power system based on random forest[J].Smart Power,2018,46(5):8-14.
[17]王志勇,郭创新,曹一家.基于模糊粗糙集和神经网络的短期负荷预测方法[J].中国电机工程学报,2005,25(19):7-11.WANG Zhiyong,GUO Chuangxin,CAO Yijia.A method for short term load forecasting integrating fuzzy-rough set with artificial neural network[J].Proceedings of the CSEE,2005,25(19):7-11.
[18]吴潇雨,和敬涵,张沛,等.基于灰色投影改进随机森林算法的电力系统短期负荷预测[J].电力系统自动化,2015,39(12):50-55.WU Xiaoyu,HE Jinghan,ZHANG Pei,et al.Power system short-term load forecasting based on improved random forest with gray relation projection[J].Automation of Electric Power Systems,2015,39(12):50-55.
[19]林芳,林焱,吕宪龙,等.基于均衡KNN算法的电力负荷短期并行预测[J].中国电力,2018,51(10):88-94.LIN Fang,LIN Yan,LüXianlong,et al.Short-term parallel power load forecasting based on balanced KNN[J].Electric Power,2018,51(10):88-94.
[20]赵芝璞,高超,沈艳霞,等.基于关联模糊神经网络和改进型蜂群算法的负荷预测方法[J].中国电力,2018,51(2):54-60.ZHAO Zhipu,GAO Chao,SHEN Yanxia,et al.A method for load forecasting based on correlated fuzzy neural network and improved artificial bee colony algorithm[J].Electric Power,2018,51(2):54-60.
[21]张炀,汪洋,祝宇翔,等.基于PAM和ELM的电力短期负荷预测相似日选取算法[J].电力大数据,2017,20(12):84-87.ZHANG Yang,WANG Yang,ZHU Yuxiang,et al.Similarity day selection algorithm for power short-term load forecasting[J].Power Systems and Big Data,2017,20(12):84-87.
[22]张冰玉.基于数据挖掘技术的短期电力负荷预测[J].电力大数据,2017,20(10):18-21.ZHANG Bingyu.Prediction of short-term power load based on data mining technology[J].Power Systems and Big Data,2017,20(10):18-21.
[23]曲正伟,张坤,王云静,等.基于PSO-OMP优化的WD-ASD超短期负荷预测[J].电工电能新技术,2017,36(12):39-45.QU Zhengwei,ZHANG Kun,WANG Yunjing,et al.Short-term load forecasting based on WD-ASD optimized by PSO-OMP[J].Advanced Technology of Electrical Engineering and Energy,2017,36(12):39-45.
[24]李啸骢,李春涛,从兰美,等.基于动态权值相似日选取算法的短期负荷预测[J].电力系统保护与控制,2017,45(6):1-8.LI Xiaocong,LI Chuntao,CONG Lanmei,et al.Shortterm load forecasting based on dynamic weight similar day selection algorithm[J].Power System Protection and Control,2017,45(6):1-8.
[25]SENJYU T,TAKARA H,UEZATO K,et al.One-hour-ahead load forecasting using neural network[J].IEEE Transactions on Power Systems,2002,17(1):113-118.
[26]孙谦,姚建刚,赵俊,等.基于最优交集相似日选取的短期母线负荷综合预测[J].中国电机工程学报,2013,33(4):126-134.SUN Qian,YAO Jiangang,ZHAO Jun,et al.Short-term bus load integrated forecasting based on selecting optimal intersection similar days[J].Proceedings of the CSEE,2013,33(4):126-134.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |