Improving the quality of load forecasts using smart meter data

Shahzadeh, Abbas, Khosravi, Abbas and Nahavandi, Saeid 2015, Improving the quality of load forecasts using smart meter data, in ICONIP 2015: Proceedings of the 22nd International Conference on Neural Information Processing, Springer, Berlin, Germany, pp. 667-674, doi: 10.1007/978-3-319-26561-2_78.

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Title Improving the quality of load forecasts using smart meter data
Author(s) Shahzadeh, Abbas
Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Conference name Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 9-12 Nov. 2015
Title of proceedings ICONIP 2015: Proceedings of the 22nd International Conference on Neural Information Processing
Publication date 2015
Series Lecture notes in computer science
Start page 667
End page 674
Total pages 8
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Smart meters
Neural networks
Load forecast
Wavelet transformation
Summary For the operator of a power system, having an accurate forecast of the day-ahead load is imperative in order to guaranty the reliability of supply and also to minimize generation costs and pollution. Furthermore, in a restructured power system, other parties, like utility companies, large consumers and in some cases even ordinary consumers, can benefit from a higher quality demand forecast. In this paper, the application of smart meter data for producing more accurate load forecasts has been discussed. First an ordinary neural network model is used to generate a forecast for the total load of a number of consumers. The results of this step are used as a benchmark for comparison with the forecast results of a more sophisticated method. In this new method, using wavelet decomposition and a clustering technique called interactive k-means, the consumers are divided into a number of clusters. Then for each cluster an individual neural network is trained. Consequently, by adding the outputs of all of the neural networks, a forecast for the total load is generated. A comparison between the forecast using a single model and the forecast generated by the proposed method, proves that smart meter data can be used to significantly improve the quality of load forecast.
ISBN 9783319265605
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-26561-2_78
Field of Research 090607 Power and Energy Systems Engineering (excl Renewable Power)
08 Information And Computing Sciences
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
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