Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9847
Title: Electric load forecasting for large office building based on radial basis function neural network
Authors: Mai, W
Chung, CY 
Wu, T
Huang, H
Keywords: Building energy efficiency
Commercial office buildings
Demand side management
Load forecasting
Issue Date: 2014
Publisher: IEEE Computer Society
Source: IEEE Power and Energy Society General Meeting, 2014, 6939378 How to cite?
Abstract: 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.
Description: 2014 IEEE Power and Energy Society General Meeting, 27-31 July 2014
URI: http://hdl.handle.net/10397/9847
ISSN: 1944-9925
DOI: 10.1109/PESGM.2014.6939378
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