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dc.contributor.authorAhmad, A
dc.contributor.authorAnderson, T
dc.date.accessioned2015-01-07T03:20:51Z
dc.date.available2015-01-07T03:20:51Z
dc.date.copyright2014-12-08
dc.identifier.citation2014 Asia-Pacific Solar Research Conference held at University of New South Wales, Sydney, 2014-12-08 to 2014-12-10, published in: Proceedings of the 2014 Asia-Pacific Solar Research Conference
dc.identifier.urihttp://hdl.handle.net/10292/8267
dc.description.abstractIn this study several Artificial Neural Network (ANN) models were experimented to predict electricity consumption for a residential house in New Zealand. The effect of number of users in the house, day of the week and weather variables on electricity consumption was analyzed. Each model has been constructed using different structures, learning algorithms and transfer functions in order to come up with the best model which has better generalizing ability. Further each model has been experimented with different number of neurons in the hidden layers and different number of delays in the tapped layers, and their effect on prediction accuracy was analyzed. Subsequently the most accurate ANN model was used to study the effects of weather predictor variables on the electricity consumption. Actual input and output data were used in the training, validation and testing process. A comparison among the developed neural network models was performed to find the most suitable model. Finally the selected ANN model has been used to predict 24 hours in advance electricity consumption for a residential house in New Zealand.
dc.publisherAustralian PV Institute (APVI)
dc.relation.urihttp://apvi.org.au/asprc-2014-papers-presentations/
dc.rightsThis report is copyright of the Australian PV Institute. The information contained therein may freely be used but all such use should cite the source as (see Citation).
dc.titleHourly electrical power consumption prediction for New Zealand residential houses using artificial neural network models
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
pubs.elements-id176913


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