Abstract
Electricity load forecasting has become increasingly important for the industry. 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.
Several non-linear techniques such as the SVM have been applied to this problem. However, the properties of the load time series change strongly with the seasons, holidays and other factors. Therefore global models such as the SVM are not suitable to predict accurately the load demand.
In this paper we propose a model that first splits the time series into homogeneous regions using the Self Organizing Maps (SOM). Next, an 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 our model outperforms several statistical and machine learning forecasting techniques.
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Martín-Merino, M., Román, J. (2006). Electricity Load Forecasting Using Self Organizing Maps. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_74
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DOI: https://doi.org/10.1007/11840930_74
Publisher Name: Springer, Berlin, Heidelberg
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