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Modeling Multi-horizon Electricity Demand Forecasts in Australia: A Term Structure Approach

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  • Stan Hurn
  • Vance Martin
  • Jing Tian

Abstract

The Australian Electricity Market Operator generates one-day ahead electricity demand forecasts for the National Electricity Market in Australia and updates these forecasts over time until the time of dispatch. Despite the fact that these forecasts play a crucial role in the decision-making process of market participants, little attention has been paid to their evaluation and interpretation. Using half-hourly data from 2011 to 2015 for New South Wales and Queensland, it is shown that the official half-hourly demand forecasts do not satisfy the econometric properties required of rational forecasts. Instead there is a relationship between forecasts and forecast horizon similar to a term structure model of interest rates. To study the term structure of demand forecasts, a factor analysis that uses a small set of latent factors to explain the common variation among multiple observables is implemented. A three-factor model is identified with the factors admitting interpretation as the level, slope and curvature of the term structure of forecasts. The validity of the model is reinforced by assessing the economic value of demand forecasts. It is demonstrated that simple adjustments to long-horizon electricity demand forecasts based on the three estimated factors can enhance the informational content of the official forecasts.

Suggested Citation

  • Stan Hurn & Vance Martin & Jing Tian, 2023. "Modeling Multi-horizon Electricity Demand Forecasts in Australia: A Term Structure Approach," The Energy Journal, , vol. 44(3), pages 251-266, May.
  • Handle: RePEc:sae:enejou:v:44:y:2023:i:3:p:251-266
    DOI: 10.5547/01956574.44.2.shur
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    References listed on IDEAS

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    1. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    2. Gregory Mankiw, N. & Shapiro, Matthew D., 1986. "Do we reject too often? : Small sample properties of tests of rational expectations models," Economics Letters, Elsevier, vol. 20(2), pages 139-145.
    3. 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, Elsevier, vol. 251(2), pages 522-530.
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