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ThinkMind // DBKDA 2012, The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications // View article dbkda_2012_2_40_30187


Towards Accurate Electricity Load Forecasting in Smart Grids

Authors:
Zeyar Aung
Mohamed Toukhy
John Williams
Abel Sanchez
Sergio Herrero

Keywords: smart grids; data mining; load forecasting; regression analysis; support vector machines

Abstract:
Smart grids, or intelligent electricity grids that utilize modern IT/communication/control technologies, become a global trend nowadays. Forecasting of future grid load (electricity usage) is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the supply and the demand of electricity. In this paper, our contribution is the proposal of a new data mining scheme to forecast the peak load of a particular consumer entity in the smart grid for a future time unit. We utilize least-squares version of support vector regression with online learning strategy in our approach. Experimental results show that our method is able to provide more accurate results than an existing forecasting method which is reported to be one of the best. Our method can provide 98.4–98.7% of average accuracy whilst the state-of-the-art method by Lv et al. is able to provide only 96.7% of average accuracy. Our method is also computationally efficient and can potentially be used for large scale load forecasting applications.

Pages: 51 to 57

Copyright: Copyright (c) IARIA, 2012

Publication date: February 29, 2012

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-185-4

Location: Saint Gilles, Reunion

Dates: from February 29, 2012 to March 5, 2012

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