基于时序分解的用电负荷分析与预测
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摘要
在智能电网普及的大数据背景下,对电力数据进行精准的分析和预测对电网规划和经济部门的管理决策具有重要的指导意义,但大多数模型都只是在单一的时间尺度上进行研究。针对这一问题提出一种基于时序分解的后向传播算法的循环神经网络预测模型。通过对真实的居民用电消费数据以及外部因素数据统计处理,深入地分析了居民用电特点以及行为规律,并根据其数据的特征以及天气、节假日等外部因素对用户用电行为的影响建立预测模型,对用户未来时段的用电量进行预测。此外,考虑到居民用电消费数据的时序特征在不同时间尺度呈现不同的变化规律,通过时序分解建立预测模型来对用户用电行为的周期性和趋势性进行建模,并通过加权融合达到一起训练的效果,具有一定的协同性,提升预测精度。
Under the big data background of smart grid popularization, accurate analysis and forecasting of power data is of great guiding significance to the planning of power grids and the management decisions of economic departments.However, most of the models are only studied on a single time scale. Aiming at this problem, a recurrent neural network prediction model based on time series decomposition back propagation algorithm is proposed. Through the statistical analysis of the real consumer electricity consumption data and external factors, the residential electricity consumption characteristics and behavior rules are analyzed in depth. According to the characteristics of the data and external factors such as weather, holidays and other factors affecting the user's electricity behavior, a forecasting model is established to predict the electricity consumption of users in the future. In addition, taking into account the temporal characteristics of household electricity consumption data showing different variation rules at different time scales, the forecasting model is established by time series decomposition to model the periodicity and trend of user's electricity consumption behavior and to achieve the goal by weighted fusion training effect, with a certain degree of synergy, improving prediction accuracy.
Under the big data background of smart grid popularization, accurate analysis and forecasting of power data is of great guiding significance to the planning of power grids and the management decisions of economic departments.However, most of the models are only studied on a single time scale. Aiming at this problem, a recurrent neural network prediction model based on time series decomposition back propagation algorithm is proposed. Through the statistical analysis of the real consumer electricity consumption data and external factors, the residential electricity consumption characteristics and behavior rules are analyzed in depth. According to the characteristics of the data and external factors such as weather, holidays and other factors affecting the user's electricity behavior, a forecasting model is established to predict the electricity consumption of users in the future. In addition, taking into account the temporal characteristics of household electricity consumption data showing different variation rules at different time scales, the forecasting model is established by time series decomposition to model the periodicity and trend of user's electricity consumption behavior and to achieve the goal by weighted fusion training effect, with a certain degree of synergy, improving prediction accuracy.
引文
[1]赵腾,张焰,张东霞.智能配电网大数据应用技术与前景分析[J].电网技术,2014,38(12):3305-3312.
[2]Zhang J,Zheng Y,Qi D.Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//AAAI,2017.
[3]Yu S,Wang K,Wei Y M.A hybrid self-adaptive particle swarm optimization-genetic algorithm-radial basis function model for annual electricity demand prediction[J].Energy Conversion&Management,2015,91:176-185.
[4]Kaytez F,Taplamacioglu M C,Cam E,et al.Forecasting electricity consumption:a comparison of regression analysis,neural networks and least squares support vector machines[J].International Journal of Electrical Power&Energy Systems,2015,67(67):431-438.
[5]赵腾,王林童,张焰,等.采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J].中国电机工程学报,2016,36(3):604-614.
[6]盛立锃,曾喆昭,李莎.基于代数多项式模型的用电量预测研究[J].电力科学与技术学报,2015(1):34-40.
[7]李婉华,陈宏,郭昆,等.基于随机森林算法的用电负荷预测研究[J].计算机工程与应用,2016,52(23):236-243.
[8]刘洋均,王清心,丁家满.电网规划指标的相关性分析及融合算法[J].计算机工程与应用,2017,53(6):241-245.
[9]Bianco V,Manca O,Nardini S.Linear regression models to forecast electricity consumption in Italy[J].Energy Sources Part B Economics Planning&Policy,2013,8(1):86-93.
[10]Arisoy I,Ozturk I.Estimating industrial and residential electricity demand in Turkey:a time varying parameter approach[J].Energy,2014,66(4):959-964.
[11]Pappas S S,Ekonomou L,Karamousantas D C,et al.Electricity demand loads modeling using AutoRegressive Moving Average(ARMA)models[J].Energy,2008,33(9):1353-1360.
[12]Erdogdu E.Electricity demand analysis using cointegration and ARIMA modelling:a case study of Turkey[J].MPRA Paper,2007,35(2):1129-1146.
[13]Wang Y,Wang J,Zhao G,et al.Application of residual modification approach in seasonal ARIMA for electricity demand forecasting:a case study of China[J].Energy Policy,2012,48(3):284-294.
[14]陈洁,蒋雪松.基于灰色预测模型的淮安市居民生活用电消费研究[J].企业经济,2015(2):162-165.
[15]陈高波.年用电量预测的PLS-LSSVM模型[J].计算机工程与应用,2010,46(25):223-225.
[16]杨政,姚尧,金小明.采用稳健回声状态网络的超短期负荷预测方法[J].计算机工程与应用,2016,52(4):250-254.
[17]覃泓皓,牛东晓,钱望月,等.考虑季节特性的改进小波包-RBF用电量预测方法[J].电力科学与技术学报,2016,31(2):37-44.
[18]徐尧强,方乐恒,赵冬华,等.基于LSTM神经网络的用电量预测[J].电力大数据,2017(8):25-29.
[19]冯雪静.基于径向神经网络的某个区用电量预测模型研究[J].工程技术:全文版,2016(7).
[2]Zhang J,Zheng Y,Qi D.Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//AAAI,2017.
[3]Yu S,Wang K,Wei Y M.A hybrid self-adaptive particle swarm optimization-genetic algorithm-radial basis function model for annual electricity demand prediction[J].Energy Conversion&Management,2015,91:176-185.
[4]Kaytez F,Taplamacioglu M C,Cam E,et al.Forecasting electricity consumption:a comparison of regression analysis,neural networks and least squares support vector machines[J].International Journal of Electrical Power&Energy Systems,2015,67(67):431-438.
[5]赵腾,王林童,张焰,等.采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J].中国电机工程学报,2016,36(3):604-614.
[6]盛立锃,曾喆昭,李莎.基于代数多项式模型的用电量预测研究[J].电力科学与技术学报,2015(1):34-40.
[7]李婉华,陈宏,郭昆,等.基于随机森林算法的用电负荷预测研究[J].计算机工程与应用,2016,52(23):236-243.
[8]刘洋均,王清心,丁家满.电网规划指标的相关性分析及融合算法[J].计算机工程与应用,2017,53(6):241-245.
[9]Bianco V,Manca O,Nardini S.Linear regression models to forecast electricity consumption in Italy[J].Energy Sources Part B Economics Planning&Policy,2013,8(1):86-93.
[10]Arisoy I,Ozturk I.Estimating industrial and residential electricity demand in Turkey:a time varying parameter approach[J].Energy,2014,66(4):959-964.
[11]Pappas S S,Ekonomou L,Karamousantas D C,et al.Electricity demand loads modeling using AutoRegressive Moving Average(ARMA)models[J].Energy,2008,33(9):1353-1360.
[12]Erdogdu E.Electricity demand analysis using cointegration and ARIMA modelling:a case study of Turkey[J].MPRA Paper,2007,35(2):1129-1146.
[13]Wang Y,Wang J,Zhao G,et al.Application of residual modification approach in seasonal ARIMA for electricity demand forecasting:a case study of China[J].Energy Policy,2012,48(3):284-294.
[14]陈洁,蒋雪松.基于灰色预测模型的淮安市居民生活用电消费研究[J].企业经济,2015(2):162-165.
[15]陈高波.年用电量预测的PLS-LSSVM模型[J].计算机工程与应用,2010,46(25):223-225.
[16]杨政,姚尧,金小明.采用稳健回声状态网络的超短期负荷预测方法[J].计算机工程与应用,2016,52(4):250-254.
[17]覃泓皓,牛东晓,钱望月,等.考虑季节特性的改进小波包-RBF用电量预测方法[J].电力科学与技术学报,2016,31(2):37-44.
[18]徐尧强,方乐恒,赵冬华,等.基于LSTM神经网络的用电量预测[J].电力大数据,2017(8):25-29.
[19]冯雪静.基于径向神经网络的某个区用电量预测模型研究[J].工程技术:全文版,2016(7).