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

Adam Clements, Stan Hurn and Zili Li ()
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Zili Li: QUT

No 103, NCER Working Paper Series from National Centre for Econometric Research

Abstract: 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 e ective 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 methods are 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.

Keywords: Short-term load forecasting; seasonality; intra-day correlation; recursive equation system (search for similar items in EconPapers)
JEL-codes: C32 Q41 Q47 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2014-09-06, Revised 2015-05-06
New Economics Papers: this item is included in nep-ene and nep-for
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Citations: View citations in EconPapers (2)

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Journal Article: Forecasting day-ahead electricity load using a multiple equation time series approach (2016)
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