The Open UniversitySkip to content

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

Haben, Stephen; Ward, Jonathan; Vukadinovic Greetham, Danica; Singleton, Colin and Grindrod, Peter (2014). A new error measure for forecasts of household-level, high resolution electrical energy consumption. International Journal of Forecasting, 30(2) pp. 246–256.

Full text available as:
PDF (Version of Record) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (755kB) | Preview
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


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 individual household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the most appropriate charging and discharging cycles. However, before such methods can be developed, validation measures which can assess the accuracy and usefulness of forecasts of the volatile and noisy household-level demand are required. 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 the 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: Journal Item
Copyright Holders: 2013 International Institute of Forecasters
ISSN: 0169-2070
Keywords: Verification methods; Load forecasting; Volatile data; Smart meter; Error measure
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 55702
Depositing User: Danica Vukadinovic Greetham
Date Deposited: 11 Feb 2019 11:30
Last Modified: 21 Mar 2020 04:32
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU