Hourly electrical power consumption prediction for New Zealand residential houses using artificial neural network models
| aut.researcher | Anderson, Timothy | |
| dc.contributor.author | Ahmad, A | |
| dc.contributor.author | Anderson, T | |
| dc.date.accessioned | 2015-01-07T03:20:51Z | |
| dc.date.available | 2015-01-07T03:20:51Z | |
| dc.date.copyright | 2014-12-08 | |
| dc.date.issued | 2014-12-08 | |
| dc.description.abstract | In 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.identifier.citation | 2014 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.uri | https://hdl.handle.net/10292/8267 | |
| dc.publisher | Australian PV Institute (APVI) | |
| dc.relation.uri | http://apvi.org.au/asprc-2014-papers-presentations/ | |
| dc.rights | This 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.rights.accessrights | OpenAccess | |
| dc.title | Hourly electrical power consumption prediction for New Zealand residential houses using artificial neural network models | |
| dc.type | Conference Contribution | |
| pubs.elements-id | 176913 | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Design & Creative Technologies | |
| pubs.organisational-data | /AUT/Design & Creative Technologies/School of Engineering |