S. F. Crone and N. Kourentzes, 2011, International Joint Conference on Neural Networks, San Jose, 31-05 August 2011.
Forecasting future electricity load represents one of the most prominent areas of electrical engineering, in which artificial neural networks (NN) are routinely applied in practice. A common approach to overcome the complexity of building NNs for high-frequency load data is to segment the time series into homogeneous subclasses of simpler subseries, often a constant hour of the day or day of the week, which are forecasted independently using a separate NN model, and which are recombined to provide a complete forecast of the next days ahead. Despite the empirical importance of load forecasting, and the high operational cost associated with forecast errors, the potential benefits of segmenting time series into subseries have not been evaluated in an empirical comparison. This paper assesses the empirical accuracy of segmenting empirical hourly load data taken from the UK into daily subseries versus forecasting the original, continuous time series with NNs. Empirical accuracy is provided in comparison to statistical benchmark algorithms and across multiple rolling time origins, which indicates the superior performance of NN on continuous, non-segmented time series, in contrast to best practices.