Abstract
The interest in electricity load forecasting has grown up in the last years. However, the accurate load prediction remains a difficult task due particularly to the non-linear character of the time series and the periodical and seasonal patterns it exhibits.
Several machine learning techniques such as the Support Vector Machines (SVM) have been developed that are able to deal with non-linear time series. However, the patterns of electricity demand change strongly and periodically with seasons, holidays and other factors. Therefore global models such as the SVM are not expected to perform well.
In this paper we propose a new segmentation algorithm based on the Self Organizing Maps (SOM) to split the time series into homogeneous regions. Next, a linear SVM is locally trained in each region.
The algorithm proposed has been applied to the prediction of the maximum daily electricity demand. The experimental results show that the new segmentation algorithm helps to improve several well known forecasting techniques.
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Martín-Merino, M., Román, J. (2006). A New SOM Algorithm for Electricity Load Forecasting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_111
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DOI: https://doi.org/10.1007/11893028_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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