Improving load forecast accuracy by clustering consumers using smart meter data Shahzadeh, Abbas, Khosravi, Abbas and Nahavandi, Saeid 2015, Improving load forecast accuracy by clustering consumers using smart meter data, in IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-7, doi: 10.1109/IJCNN.2015.7280393. Attached Files Name Description MIMEType Size Downloads Title Improving load forecast accuracy by clustering consumers using smart meter data Author(s) Shahzadeh, Abbas Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name The International Joint Conference on Neural Networks (2015: Killarney, Ireland) Conference location Killarney, Ireland Conference dates 12-17 Jul. 2015 Title of proceedings IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks Publication date 2015 Start page 1 End page 7 Total pages 7 Publisher IEEE Place of publication Piscataway, N.J. Summary Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated. ISBN 9781479919604 Language eng DOI 10.1109/IJCNN.2015.7280393 Field of Research 090607 Power and Energy Systems Engineering (excl Renewable Power) Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30082895 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Apr 2016, 12:20:13 EST Mon, 18 Apr 2016, 12:21:40 EST Mon, 18 Apr 2016, 12:46:15 EST Mon, 18 Apr 2016, 13:33:34 EST Thu, 28 Jul 2016, 10:53:07 EST Thu, 28 Jul 2016, 10:55:49 EST Wed, 12 Oct 2016, 15:06:04 EST Wed, 01 Mar 2017, 07:55:55 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 31 times in Scopus Search Google Scholar Access Statistics: 515 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 18 Apr 2016, 12:21:40 EST

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