Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

作     者：Si Chen Yaxing Ren Daniel Friedrich Zhibin Yu James Yu 

作者机构：James Watt School of Engineeringthe University of GlasgowGlasgow G128QQUK School of EngineeringThe University of EdinburghEdinburgh EH93FBUK SP Distribution PLCGlasgow G720HTUK 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2021年第5卷第3期

页      面：159-170页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：SP Distribution PLC campus energy consumption ScottishPower Renewables, (146) ScottishPower Renewables 

主　　题：Building energy Electricity demand prediction Statistical modelling Artificial neural network Occupancy rate 

摘      要：Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,*** makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same *** order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office *** first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’*** finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted *** second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather *** this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather *** proposed approaches are verified by the real data from the University of Glasgow as a case *** simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity *** addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction *** addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without r