A new error measure for forecasts of household-level, high resolution electrical energy consumption

Haben, S., Ward, J., Vukadinovic Greetham, D., Singleton, C. and Grindrod, P. (2014) A new error measure for forecasts of household-level, high resolution electrical energy consumption. International Journal of Forecasting, 30 (2). pp. 246-256. ISSN 0169-2070 doi: 10.1016/j.ijforecast.2013.08.002

Abstract/Summary

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/33647
Identification Number/DOI	10.1016/j.ijforecast.2013.08.002
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Centre for the Mathematics of Human Behaviour (CMOHB)
Publisher	Elsevier
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