基于季节调整和Holt-Winters的月度负荷预测方法
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摘要
针对负荷序列中异常数据会导致模型误设或参数估计发生偏差的问题,提出利用季节调整方法,先对原始负荷序列进行季节调整,获得消除离群值、节假日影响的季节调整后序列和季节成分序列;然后用改进的HoltWinters方法对季节调整后成分进行预测,用虚拟回归方法预测季节成分序列;最后对各成分预测结果重构得到最终预测结果的月度负荷预测方法。通过实例检验,提出的方法能明显提高预测精度,预测效果要优于季节性Holt-Winters、SARIMA、神经网络、支持向量机等模型。
Load forecasting plays an important role in the planning and economic and secure operation of power systems.However,the abnormal data in load series will result in forecasting model misspecification or incorrect model parameters estimation.So a hybrid monthly load forecasting model based on seasonal adjustment and improved holt-winters is built to solve such problems.Firstly,after seasonal adjustment,the final seasonally adjusted series where outliers or holidays effects have been removed and seasonal component series can be obtained simultaneously;secondly,the improved Holt-Winters method is used to forecast final seasonally adjusted component,and virtual regression equation is used to forecast seasonal component.Finally,the final forecasting result can be obtained by using forecasting result of seasonal component and seasonal adjusted component jointly.The case calculation results show that the proposed method can significantly improve the prediction accuracy and the forecasting performance is better than seasonal Holt-Winters,SARIMA,neural network,and support vector machine.In summary,the proposed model can be practically applied as a monthly load forecasting tool.
Load forecasting plays an important role in the planning and economic and secure operation of power systems.However,the abnormal data in load series will result in forecasting model misspecification or incorrect model parameters estimation.So a hybrid monthly load forecasting model based on seasonal adjustment and improved holt-winters is built to solve such problems.Firstly,after seasonal adjustment,the final seasonally adjusted series where outliers or holidays effects have been removed and seasonal component series can be obtained simultaneously;secondly,the improved Holt-Winters method is used to forecast final seasonally adjusted component,and virtual regression equation is used to forecast seasonal component.Finally,the final forecasting result can be obtained by using forecasting result of seasonal component and seasonal adjusted component jointly.The case calculation results show that the proposed method can significantly improve the prediction accuracy and the forecasting performance is better than seasonal Holt-Winters,SARIMA,neural network,and support vector machine.In summary,the proposed model can be practically applied as a monthly load forecasting tool.
引文
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[13]乔占俊.基于Census X12-SARIMA模型的中长期负荷预测[J].电力系统及其自动化学报.2014,28(1):34-38.
[14]郭鸿业,陈启鑫,夏清,等.考虑经济因素时滞效应的月度负荷预测方法[J].电网技术.2016,40(2):514-520.
[15]颜伟,程超,薛斌,等.结合X12乘法模型和ARIMA模型的月售电量预测方法[J].电力系统及其自动化学报.2016,28(5):74-80.
[16]贺凤羊,刘建平.如何对中国CPI进行季节调整-基于X-12-ARIMA方法的改进[J].数量经济技术经济研究,2011(5):110-124.
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[18]Gelper S,Fried R,Croux C.Robust forecasting with exponential and Holt-Winters smoothing[J].Journal of Forecasting.2010.29(3):285-300.
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[20]吴德会.动态指数平滑预测方法及其应用[J].系统管理学报,2008,17(2):151-155.
[21]龙勇,苏振宇.霍尔特-温特斯加法模型初始值计算方法及应用[J].统计与决策,2014,(11):16-18.
[22]龙勇,苏振宇,盖晓平.成分分解方法预测月度电力负荷[J].电力系统及其自动化学报,2017,(5):35-40.
[23]Cao Guohua,Wu Lijuan.Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting[J].Energy,2016,115:734-745.
[2]Apadula F,Bassini A,Elli A,et al.Relationships between meteorological variables and monthly electr-icity demand[J].Applied Energy,2012,98(5):346-356.
[3]Chang Y,Chang S K,Miller J I,et al.A new approach to modeling the effects of temperature fluctuations on monthly electricity demand[J].Energy Economics,2016,60:206-216.
[4]Wang Yuanyuan,Wang Jianzhou,Zhao Ge,et al.Application of residual modification approach in seasonal ARI-MA for electricity demand forecasting:A case study of China[J].Energy Policy,2012,48(3):284-294.
[5]González-Romera E,Jaramillo Morán M A,Carmona Fernández D.Forecasting of the electric energy demand trend and monthly fluctuation with neural networks[J].Computers&Industrial Engineering,2007,52(3):336-343.
[6]牛东晓,乞建勋,邢棉.二重趋势性季节型电力负荷预测组合灰色神经网络模型[J].中国管理科学,2001,9(6):15-20.
[7]Wang Jianzhou,Zhu Wenjin,Zhang Wenyu,et al.A trend fixed on firstly and seasonal adjustment model combined with theε-SVR for short-term forecasting of electricity demand[J].Energy Policy,2009,37(11):4901-4909.
[8]毛李帆,姚建刚,金永顺,等.中长期负荷预测的异常数据辨识与缺失数据处理[J].电网技术,2010,320(7):148-153.
[9]邵臻,杨善林,高飞,等.基于可变区间权重的中期用电量半参数预测模型[J].中国管理科学,2015,23(3):123-129.
[10]梁小珍,乔晗,张珣.基于奇异谱分析的我国航空客运量集成预测模型[J].系统工程理论与实践,2017,37(6):1479-1488.
[11]陈荣,梁昌勇,陆文星,等.面向旅游突发事件的客流量混合预测方法研究[J].中国管理科学,2017,25(5):167-174.
[12]陈彦晖,刘斌.基于广义等高线的灰色波形预测模型及其应用[J].中国管理科学,2017,25(8):134-139.
[13]乔占俊.基于Census X12-SARIMA模型的中长期负荷预测[J].电力系统及其自动化学报.2014,28(1):34-38.
[14]郭鸿业,陈启鑫,夏清,等.考虑经济因素时滞效应的月度负荷预测方法[J].电网技术.2016,40(2):514-520.
[15]颜伟,程超,薛斌,等.结合X12乘法模型和ARIMA模型的月售电量预测方法[J].电力系统及其自动化学报.2016,28(5):74-80.
[16]贺凤羊,刘建平.如何对中国CPI进行季节调整-基于X-12-ARIMA方法的改进[J].数量经济技术经济研究,2011(5):110-124.
[17]王群勇,武娜.中国月度数据的季节调整:一个新方案[J].统计研究,2010,27(8):8-13.
[18]Gelper S,Fried R,Croux C.Robust forecasting with exponential and Holt-Winters smoothing[J].Journal of Forecasting.2010.29(3):285-300.
[19]何大四,张旭.改进的季节性指数平滑法预测空调负荷分析[J].同济大学学报自然科学版,2005,33(12):1672-1676.
[20]吴德会.动态指数平滑预测方法及其应用[J].系统管理学报,2008,17(2):151-155.
[21]龙勇,苏振宇.霍尔特-温特斯加法模型初始值计算方法及应用[J].统计与决策,2014,(11):16-18.
[22]龙勇,苏振宇,盖晓平.成分分解方法预测月度电力负荷[J].电力系统及其自动化学报,2017,(5):35-40.
[23]Cao Guohua,Wu Lijuan.Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting[J].Energy,2016,115:734-745.
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