Self-organization as an iterative kernel smoothing process
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Self-Organizing Maps
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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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.