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Improving the quality of load forecasts using smart meter data
conference contribution
posted on 2015-01-01, 00:00 authored by Abbas Shahzadeh, Abbas KhosraviAbbas Khosravi, Saeid NahavandiFor 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.
History
Event
Neural Information Processing. International Conference (22nd : 2015 : Istanbul, Turkey)Volume
9492Series
Lecture notes in computer sciencePagination
667 - 674Publisher
SpringerLocation
Istanbul, TurkeyPlace of publication
Berlin, GermanyPublisher DOI
Start date
2015-11-09End date
2015-11-12ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319265605Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
ICONIP 2015: Proceedings of the 22nd International Conference on Neural Information ProcessingUsage metrics
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