Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2011.03.039
Title: A SOM-based hybrid linear-neural model for short-term load forecasting
Authors: Yadav, V.
Srinivasan, D. 
Keywords: Feedforward neural network
Ho-Kashyap algorithm
Self-organizing map (SOM)
Short-term load forecasting
Issue Date: Oct-2011
Source: Yadav, V., Srinivasan, D. (2011-10). A SOM-based hybrid linear-neural model for short-term load forecasting. Neurocomputing 74 (17) : 2874-2885. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2011.03.039
Abstract: In this paper, a short-term load forecasting method is considered, which is based upon a flexible smooth transition autoregressive (STAR) model. The described model is a linear model with time varying coefficients, which are the outputs of a single hidden layer feedforward neural network. The hidden layer is responsible for partitioning the input space into multiple sub-spaces through multivariate thresholds and smooth transition between the sub-spaces. In this paper, we propose a new method to smartly initialize the weights of the hidden layer of the neural network before its training. A self-organizing map (SOM) network is applied to split the historical data dynamics into clusters, and the Ho-Kashyap algorithm is then used to obtain the separating planes' equations. Applied to the electricity markets, the proposed method is better able to model the smooth transitions between the different regimes, which are present in the load demand series because of market effects and season effects. We use data from three electricity markets to compare the prediction accuracy of the proposed method with traditional benchmarks and other recent models, and find our results to be competitive. © 2011 Elsevier B.V.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/54817
ISSN: 09252312
DOI: 10.1016/j.neucom.2011.03.039
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