基于移动人流数据的商业楼宇负荷用电预测
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摘要
建筑能耗的准确预测为电网系统进行高效管理和合理分配电能资源奠定了重要基础,而结合外因数据预测商业楼宇的负荷用电量是一个难点问题。根据建筑内人流量对用电负荷的影响,提出一种将融合移动人流数据的多维时间序列预测模型LMO(Linear Model with Occupancy)。利用楼宇负荷用电和移动人流时间序列的耦合性,LMO融合了多维特征,提高楼宇用电量预测的准确性。实验结果表明,LMO能够引入更多的先验知识,减少不确定性。因此,相比于传统方法,该预测方法具有更高的预测精度。
Accurate prediction of building energy consumption lays an important foundation for efficient management of power grid systems and rational allocation of power resources. It is a difficult problem to predict the load power consumption of commercial buildings combining external data. According to the influence of people flow on electricity load in buildings, this paper proposed a multi-dimensional time series prediction model which integrated mobile people data——linear model with occupancy(LMO). Using the coupling of building load power and mobile people flow time series, LMO combined multi-dimensional features to improve the accuracy of building electricity consumption prediction. Experimental results show that LMO can introduce more prior knowledge and reduce uncertainty. Therefore, compared with the traditional method, the prediction method has higher prediction accuracy.
Accurate prediction of building energy consumption lays an important foundation for efficient management of power grid systems and rational allocation of power resources. It is a difficult problem to predict the load power consumption of commercial buildings combining external data. According to the influence of people flow on electricity load in buildings, this paper proposed a multi-dimensional time series prediction model which integrated mobile people data——linear model with occupancy(LMO). Using the coupling of building load power and mobile people flow time series, LMO combined multi-dimensional features to improve the accuracy of building electricity consumption prediction. Experimental results show that LMO can introduce more prior knowledge and reduce uncertainty. Therefore, compared with the traditional method, the prediction method has higher prediction accuracy.
引文
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[17]Song K,Kwon N,Anderson K,et al.Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups[J].Energy and Buildings,2017,156:121-133.
[18]Wang Z,Wang Y,Srinivasan R S.A novel ensemble learning approach to support building energy use prediction[J].Energy and Buildings,2018,159:109-122.
[2]Monfet D,Corsi M,Choinière D,et al.Development of an energy prediction tool for commercial buildings using casebased reasoning[J].Energy and Buildings,2014,81:152-160.
[3]Yang J,Rivard H,Zmeureanu R.On-line building energy prediction using adaptive artificial neural networks[J].Energy and buildings,2005,37(12):1250-1259.
[4]Jovanovi? R Z,Sretenovi? A A,Zivkovi? B D.Ensemble of various neural networks for prediction of heating energy consumption[J].Energy and Buildings,2015,94:189-199.
[5]Neto A H,Fiorelli F A S.Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption[J].Energy and buildings,2008,40(12):2169-2176.
[6]Virote J,Neves-Silva R.Stochastic models for building energy prediction based on occupant behavior assessment[J].Energy and Buildings,2012,53:183-193.
[7]顾中煊,杨石,罗淑湘,等.区域建筑能耗预测模型和方法研究[J].建筑技术,2017,48(12):1240-1243.
[8]刘倩,杨昌智,胡攀.长沙市商场建筑能耗影响因素与预测模型构建研究[J].建筑科学,2017,33(12):96-102,109.
[9]Kwok S S K,Yuen R K K,Lee E W M.An intelligent approach to assessing the effect of building occupancy on building cooling load prediction[J].Building and Environment,2011,46(8):1681-1690.
[10]Yezioro A,Dong B,Leite F.An applied artificial intelligence approach towards assessing building performance simulation tools[J].Energy and Buildings,2008,40(4):612-620.
[11]方涛涛,马小军,陈冲.基于BP-Adaboost算法的建筑能耗预测研究[J].科技通报,2017,33(7):162-166.
[12]Li K,Hu C,Liu G,et al.Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis[J].Energy and Buildings,2015,108:106-113.
[13]Sandels C,Widén J,Nordstr9m L,et al.Day-ahead predictions of electricity consumption in a Swedish office building from weather,occupancy,and temporal data[J].Energy and Buildings,2015,108:279-290.
[14]Wang Z,Ding Y.An occupant-based energy consumption prediction model for office equipment[J].Energy and Buildings,2015,109:12-22.
[15]Walter T,Price P N,Sohn M D.Uncertainty estimation improves energy measurement and verification procedures[J].Applied Energy,2014,130:230-236.
[16]Liang X,Hong T,Shen G Q.Improving the accuracy of energy baseline models for commercial buildings with occupancy data[J].Applied energy,2016,179:247-260.
[17]Song K,Kwon N,Anderson K,et al.Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups[J].Energy and Buildings,2017,156:121-133.
[18]Wang Z,Wang Y,Srinivasan R S.A novel ensemble learning approach to support building energy use prediction[J].Energy and Buildings,2018,159:109-122.