An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting

Authors:
Henrique S. Hippert;James W. Taylor
Affiliations:
Universidade Federal de Juiz de Fora, Brazil;Saïd Business School, University of Oxford, UK
Venue:
Neural Networks
Year:
2010

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Abstract

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation.