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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.

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Title Improving load forecast accuracy by clustering consumers using smart meter data
Author(s) Shahzadeh, Abbas
Khosravi, Abbas
Nahavandi, Saeid
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
Collection: Centre for Intelligent Systems Research
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