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Electricity Load Forecasting Using Machine Learning Techniques

Electricity Load Forecasting Using Machine Learning Techniques

Manuel Martín-Merino Acera
Copyright: © 2010 |Pages: 19
ISBN13: 9781615206292|ISBN10: 1615206299|ISBN13 Softcover: 9781616923105|EISBN13: 9781615206308
DOI: 10.4018/978-1-61520-629-2.ch017
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MLA

Acera, Manuel Martín-Merino. "Electricity Load Forecasting Using Machine Learning Techniques." Business Intelligence in Economic Forecasting: Technologies and Techniques, edited by Jue Wang and Shouyang Wang, IGI Global, 2010, pp. 318-336. https://doi.org/10.4018/978-1-61520-629-2.ch017

APA

Acera, M. M. (2010). Electricity Load Forecasting Using Machine Learning Techniques. In J. Wang & S. Wang (Eds.), Business Intelligence in Economic Forecasting: Technologies and Techniques (pp. 318-336). IGI Global. https://doi.org/10.4018/978-1-61520-629-2.ch017

Chicago

Acera, Manuel Martín-Merino. "Electricity Load Forecasting Using Machine Learning Techniques." In Business Intelligence in Economic Forecasting: Technologies and Techniques, edited by Jue Wang and Shouyang Wang, 318-336. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-629-2.ch017

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Abstract

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.

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