Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9847
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dc.contributorDepartment of Electrical Engineering-
dc.creatorMai, W-
dc.creatorChung, CY-
dc.creatorWu, T-
dc.creatorHuang, H-
dc.date.accessioned2015-10-13T08:26:58Z-
dc.date.available2015-10-13T08:26:58Z-
dc.identifier.issn1944-9925-
dc.identifier.urihttp://hdl.handle.net/10397/9847-
dc.description2014 IEEE Power and Energy Society General Meeting, 27-31 July 2014en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.subjectBuilding energy efficiencyen_US
dc.subjectCommercial office buildingsen_US
dc.subjectDemand side managementen_US
dc.subjectLoad forecastingen_US
dc.titleElectric load forecasting for large office building based on radial basis function neural networken_US
dc.typeConference Paperen_US
dc.identifier.volume2014-October-
dc.identifier.doi10.1109/PESGM.2014.6939378-
dcterms.abstractThe 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.-
dcterms.bibliographicCitationIEEE Power and Energy Society General Meeting, 2014, 6939378-
dcterms.issued2014-
dc.identifier.scopus2-s2.0-84930999562-
dc.relation.ispartofbookIEEE Power and Energy Society General Meeting-
dc.identifier.rosgroupid2014004048-
dc.description.ros2014-2015 > Academic research: refereed > Refereed conference paper-
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