Abstract
This paper develops a short-term forecasting system for hourly electricity load demand based on Unobserved Components set up in a State Space framework. The system consists of two options, a univariate model and a non-linear bivariate model that relates demand to temperature. In order to handle the rapidly sampling interval of the data, a multi-rate approach is implemented with models estimated at different frequencies, some of them with ‘periodically amplitude modulated’ properties. The non-linear relation between demand and temperature is identified via a Data-Based Mechanistic approach and finally implemented by Radial Basis Functions. The models also include signal extraction of daily and weekly components. Both models are tested on the basis of a thorough experiment in which other options, like ARIMA and Artificial Neural Networks are also used. The models proposed compare very favourably with the rest of alternatives in forecasting load demand.
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Acknowledgements
We are most grateful to the UK Engineering and Physical and Science Research Council (EPSRC: Grant ESA7088) and two British Companies that provided the data used in this project. We also would like to thank two anonymous referees and the editor's comments on a previous version of this paper. Readers interested in further technical details may ask the authors for an expanded version of this paper.
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Pedregal, D., Young, P. Development of improved adaptive approaches to electricity demand forecasting. J Oper Res Soc 59, 1066–1076 (2008). https://doi.org/10.1057/palgrave.jors.2602447
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DOI: https://doi.org/10.1057/palgrave.jors.2602447