基于时间分解技术的中远期逐时负荷预测模型
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摘要
中远期电力负荷预测对于引导电网规划建设和提升电力系统资源优化配置具有重要意义。为解决当前中远期负荷预测时间尺度过大、预测精度有限的问题,利用时间分解技术,对电力负荷的长期趋势与短期特征分别进行建模分析,从而提出一种新型预测方法,将中远期负荷预测的时间尺度缩短至小时,实现中远期逐时负荷预测。算例分析表明,所建模型在中远期逐时负荷预测方面的性能优于现有的同类模型,具有较高的全局精度和稳定性。同时,能够有效呈现电力负荷的概率密度特征和极值特性,有望为中远期电力规划提供参考。
Mid-long term load forecasting is of great significance for guiding the planning and construction of the power grid and improving the optimal allocation of power system resources. At present, the time scale of the medium and long term load forecasting is too large and the prediction precision is limited. To overcome this defect, this paper uses time decomposition technology to respectively analyze the long-term trend and short-term characteristics of power load, and then proposes a new prediction method, which will shorten the time scale of mid-long term load forecasting to hours and achieve mid-long term hourly load forecasting. The case study shows that the performance of the proposed model is better than the existing similar models. It has high global accuracy and stability. At the same time, it can effectively present the probability density characteristics and extreme value characteristics of power load. The proposed method is expected to provide reference for mid-long term planning of power grid.
Mid-long term load forecasting is of great significance for guiding the planning and construction of the power grid and improving the optimal allocation of power system resources. At present, the time scale of the medium and long term load forecasting is too large and the prediction precision is limited. To overcome this defect, this paper uses time decomposition technology to respectively analyze the long-term trend and short-term characteristics of power load, and then proposes a new prediction method, which will shorten the time scale of mid-long term load forecasting to hours and achieve mid-long term hourly load forecasting. The case study shows that the performance of the proposed model is better than the existing similar models. It has high global accuracy and stability. At the same time, it can effectively present the probability density characteristics and extreme value characteristics of power load. The proposed method is expected to provide reference for mid-long term planning of power grid.
引文
[1]杨慧霞,邓迎君,刘志斌,等.含有历史不良数据的电力负荷预测研究[J].电力系统保护与控制,2017,45(15):62-68.YANG Huixia,DENG Yingjun,LIU Zhibin,et al.Study on electric load forecasting with historical bad data[J].Power System Protection and Control,2017,45(15):62-68.
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[5]王大鹏,汪秉文.基于变权缓冲灰色模型的中长期负荷预测[J].电网技术,2013,37(1):167-171.WANG Dapeng,WANG Bingwen.Medium-and longterm load forecasting based on variable weights buffer grey model[J].Power System Technology,2013,37(1):167-171.
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[8]NIU Dongxiao,MA Tiannan,LIU Bingyi.Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm[J].Journal of Combinatorial Optimization,2017,33(3):1122-1143.
[9]马哲,舒勤.基于ESPRIT分解算法的短期电力负荷预测[J].电力系统保护与控制,2015,43(7):90-96.MA Zhe,SHU Qin.Short term load forecasting based on ESPRIT integrated algorithm[J].Power System Protection and Control,2015,43(7):90-96.
[10]崔和瑞,彭旭.基于ARIMAX模型的夏季短期电力负荷预测[J].电力系统保护与控制,2015,43(4):108-114.CUI Herui,PENG Xu.Summer short-term load forecasting based on ARIMAX model[J].Power System Protection and Control,2015,43(4):108-114.
[11]张健美,周步祥,林楠,等.灰色Elman神经网络的电网中长期负荷预测[J].电力系统及其自动化学报,2013,25(4):145-149.ZHANG Jianmei,ZHOU Buxiang,LIN Nan,et al.Prediction of mid-long term load based on gray Elman neural networks[J].Proceedings of the CSU-EPSA,2013,25(4):145-149.
[12]程潜善,方华亮.一种应用大数据技术的中长期负荷预测方法[J].武汉大学学报(工学版),2017,50(2):239-244.CHENG Qianshan,FANG Hualiang.A prediction method for mid-long term load forecasting using big data technology[J].Engineering Journal of Wuhan University,2017,50(2):239-244.
[13]袁铁江,袁建党,晁勤,等.电力系统中长期负荷预测综合模型研究[J].电力系统保护与控制,2012,40(14):143-146,151.YUAN Tiejiang,YUAN Jiandang,CHAO Qin,et al.Study on the comprehensive model of mid-long term load forecasting[J].Power System Protection and Control,2012,40(14):143-146,151.
[14]徐源,程潜善,李阳,等.基于大数据聚类的电力系统中长期负荷预测[J].电力系统及其自动化学报,2017,29(8):43-48.XU Yuan,CHENG Qianshan,LI Yang,et al.Mid-long term load forecasting of power system based on big data clustering[J].Proceedings of the CSU-EPSA,2017,29(8):43-48.
[15]张栋梁,严健,李晓波,等.基于马尔可夫链筛选组合预测模型的中长期负荷预测方法[J].电力系统保护与控制,2016,44(12):63-67.ZHANG Dongliang,YAN Jian,LI Xiaobo,et al.Mid-long term load forecasting based on Markov chain screening combination forecasting models[J].Power System Protection and Control,2016,44(12):63-67.
[16]熊军华,牛珂,张春歌,等.基于小波变异果蝇优化支持向量机短期负荷预测方法研究[J].电力系统保护与控制,2017,45(13):71-77.XIONG Junhua,NIU Ke,ZHANG Chunge,et al.LSSVM in short-term load forecasting based on wavelet transform and mutant fruit fly optimization algorithm[J].Power System Protection and Control,2017,45(13):71-77.
[17]HYNDMAN R J,FAN S.Density forecasting for long-term peak electricity demand[J].IEEE Transactions on Power Systems,2010,25:1142-1453.
[18]SOARES L J,MEDEIROS M C.Forecasting electricity demand using generalized long memory[J].International Journal of Forecasting,2006,22:17-28.
[19]陈伟,乐丽琴,崔凯,等.典型用户负荷特性及用电特点分析[J].能源技术经济,2011,23(9):44-49.CHEN Wei,YUE Liqin,CUI Kai,et al.Analysis on power load and consumption characteristics of representative consumers[J].Energy Technology and Economics,2011,23(9):44-49.
[20]BOOT J,FEIBES W,LISMAN J.Further methods of derivation of quarterly figures from annual data[J].Journal of the Royal Statistical Society,1967,16(1):65-75.
[21]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.
[22]MORAL-CARCEDO J,PEREZ-GARCIA J.Integrating long-term economic scenarios into peak load forecasting:an application to Spain[J].Energy,2017,140:682-695.
[23]SAMARASINGHE S.Neural networks for applied sciences and engineering[M].Auerbach Publications,2006.
[2]潘超,鲁宝春,孙丽颖,等.电力系统负荷预测方法综述[J].辽宁工业大学学报(自然科学版),2016,36(5):296-299.PAN Chao,LU Baochun,SUN Liying,et al.Review of load forecasting methods in power system[J].Journal of Liaoning University of Technology(Natural Science Edition),2016,36(5):296-299.
[3]BIANCO O,MANCA S,NARDINI.Linear regression models to forecast electricity consumption in Italy[J].Energy Sources,2013(8):86-93.
[4]邹品晶,姚建刚,孔维辉,等.基于多变量时间序列反演自记忆模型的中长期电力负荷预测[J].电力系统及其自动化学报,2017,29(10):98-105.ZOU Pinjing,YAO Jiangang,KONG Weihui,et al.Midlong term power load forecasting based on multivariable time series inversion self-memory model[J].Proceedings of the CSU-EPSA,2017,29(10):98-105.
[5]王大鹏,汪秉文.基于变权缓冲灰色模型的中长期负荷预测[J].电网技术,2013,37(1):167-171.WANG Dapeng,WANG Bingwen.Medium-and longterm load forecasting based on variable weights buffer grey model[J].Power System Technology,2013,37(1):167-171.
[6]李莉,刘建勋,刘崇新.基于改进型遗传算法的混沌神经网络在电力负荷预测的应用[J].华中电力,2010,23(2):13-17.LI Li,LIU Jianxun,LIU Chongxin.Application of chaotic neural network based on improved genetic algorithm in load prediction[J].Central China Electric Power,2010,23(2):13-17.
[7]李瑾,刘金朋,王建军.采用支持向量机和模拟退火算法的中长期负荷预测方法[J].中国电机工程学报,2011,31(16):63-66.LI Jin,LIU Jinpeng,WANG Jianjun.Mid-long term load forecasting based on simulated annealing and SVMalgorithm[J].Proceedings of the CSEE,2011,31(16):63-66.
[8]NIU Dongxiao,MA Tiannan,LIU Bingyi.Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm[J].Journal of Combinatorial Optimization,2017,33(3):1122-1143.
[9]马哲,舒勤.基于ESPRIT分解算法的短期电力负荷预测[J].电力系统保护与控制,2015,43(7):90-96.MA Zhe,SHU Qin.Short term load forecasting based on ESPRIT integrated algorithm[J].Power System Protection and Control,2015,43(7):90-96.
[10]崔和瑞,彭旭.基于ARIMAX模型的夏季短期电力负荷预测[J].电力系统保护与控制,2015,43(4):108-114.CUI Herui,PENG Xu.Summer short-term load forecasting based on ARIMAX model[J].Power System Protection and Control,2015,43(4):108-114.
[11]张健美,周步祥,林楠,等.灰色Elman神经网络的电网中长期负荷预测[J].电力系统及其自动化学报,2013,25(4):145-149.ZHANG Jianmei,ZHOU Buxiang,LIN Nan,et al.Prediction of mid-long term load based on gray Elman neural networks[J].Proceedings of the CSU-EPSA,2013,25(4):145-149.
[12]程潜善,方华亮.一种应用大数据技术的中长期负荷预测方法[J].武汉大学学报(工学版),2017,50(2):239-244.CHENG Qianshan,FANG Hualiang.A prediction method for mid-long term load forecasting using big data technology[J].Engineering Journal of Wuhan University,2017,50(2):239-244.
[13]袁铁江,袁建党,晁勤,等.电力系统中长期负荷预测综合模型研究[J].电力系统保护与控制,2012,40(14):143-146,151.YUAN Tiejiang,YUAN Jiandang,CHAO Qin,et al.Study on the comprehensive model of mid-long term load forecasting[J].Power System Protection and Control,2012,40(14):143-146,151.
[14]徐源,程潜善,李阳,等.基于大数据聚类的电力系统中长期负荷预测[J].电力系统及其自动化学报,2017,29(8):43-48.XU Yuan,CHENG Qianshan,LI Yang,et al.Mid-long term load forecasting of power system based on big data clustering[J].Proceedings of the CSU-EPSA,2017,29(8):43-48.
[15]张栋梁,严健,李晓波,等.基于马尔可夫链筛选组合预测模型的中长期负荷预测方法[J].电力系统保护与控制,2016,44(12):63-67.ZHANG Dongliang,YAN Jian,LI Xiaobo,et al.Mid-long term load forecasting based on Markov chain screening combination forecasting models[J].Power System Protection and Control,2016,44(12):63-67.
[16]熊军华,牛珂,张春歌,等.基于小波变异果蝇优化支持向量机短期负荷预测方法研究[J].电力系统保护与控制,2017,45(13):71-77.XIONG Junhua,NIU Ke,ZHANG Chunge,et al.LSSVM in short-term load forecasting based on wavelet transform and mutant fruit fly optimization algorithm[J].Power System Protection and Control,2017,45(13):71-77.
[17]HYNDMAN R J,FAN S.Density forecasting for long-term peak electricity demand[J].IEEE Transactions on Power Systems,2010,25:1142-1453.
[18]SOARES L J,MEDEIROS M C.Forecasting electricity demand using generalized long memory[J].International Journal of Forecasting,2006,22:17-28.
[19]陈伟,乐丽琴,崔凯,等.典型用户负荷特性及用电特点分析[J].能源技术经济,2011,23(9):44-49.CHEN Wei,YUE Liqin,CUI Kai,et al.Analysis on power load and consumption characteristics of representative consumers[J].Energy Technology and Economics,2011,23(9):44-49.
[20]BOOT J,FEIBES W,LISMAN J.Further methods of derivation of quarterly figures from annual data[J].Journal of the Royal Statistical Society,1967,16(1):65-75.
[21]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.
[22]MORAL-CARCEDO J,PEREZ-GARCIA J.Integrating long-term economic scenarios into peak load forecasting:an application to Spain[J].Energy,2017,140:682-695.
[23]SAMARASINGHE S.Neural networks for applied sciences and engineering[M].Auerbach Publications,2006.