Forecasting day-ahead electricity load using a multiple equation time series approach

Clements, A.E., Hurn, A.S., & Li, Z. (2016) Forecasting day-ahead electricity load using a multiple equation time series approach. European Journal of Operational Research, 251(2), pp. 522-530.

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The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

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ID Code: 95170
Item Type: Journal Article
Refereed: Yes
Keywords: Short-term load forecasting, Modelling seasonality, Intra-day load correlation
DOI: 10.1016/j.ejor.2015.12.030
ISSN: 0377-2217
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2015 Elsevier B.V.
Copyright Statement: This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Deposited On: 27 Apr 2016 01:48
Last Modified: 30 Oct 2016 22:46

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