TY - GEN
T1 - Electric load forecasting for large office building based on radial basis function neural network
AU - Mai, Weijie
AU - Chung, C. Y.
AU - Wu, Ting
AU - Huang, Huazhang
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/29
Y1 - 2014/10/29
N2 - The concept of smart grid has enabled many innovative initiatives that focus on boosting building energy efficiency such as intelligent optimal control of building energy systems and demand side management, which require accurate building load prediction. In this study, we present an hourly electric load forecasting model for large commercial office buildings based on radial basis function neural network (RBFNN) using outdoor weather data and historical load data as inputs, which is easy to implement, without tedious trial-and-error parameterizing procedures. Data from a real building under different weather conditions is used to evaluate the performance of the model and promising results are obtained, which demonstrates that the proposed method is able to precisely predict the evolving hourly electric load of the building.
AB - The concept of smart grid has enabled many innovative initiatives that focus on boosting building energy efficiency such as intelligent optimal control of building energy systems and demand side management, which require accurate building load prediction. In this study, we present an hourly electric load forecasting model for large commercial office buildings based on radial basis function neural network (RBFNN) using outdoor weather data and historical load data as inputs, which is easy to implement, without tedious trial-and-error parameterizing procedures. Data from a real building under different weather conditions is used to evaluate the performance of the model and promising results are obtained, which demonstrates that the proposed method is able to precisely predict the evolving hourly electric load of the building.
KW - building energy efficiency
KW - commercial office buildings
KW - demand side management
KW - Load forecasting
UR - http://www.scopus.com/inward/record.url?scp=84930999562&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2014.6939378
DO - 10.1109/PESGM.2014.6939378
M3 - Conference article published in proceeding or book
AN - SCOPUS:84930999562
T3 - IEEE Power and Energy Society General Meeting
BT - 2014 IEEE PES General Meeting / Conference and Exposition
PB - IEEE Computer Society
T2 - 2014 IEEE Power and Energy Society General Meeting
Y2 - 27 July 2014 through 31 July 2014
ER -