Constructing Interval Forecasts for Solar and Wind Energy Using Quantile Regression, ARCH and Exponential Smoothing Methods

John Boland ()
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John Boland: Industrial AI Research Centre, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, SA 5095, Australia

Energies, 2024, vol. 17, issue 13, 1-17

Abstract: The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications.

Keywords: probabilistic forecasting; prediction intervals; ARCH/GARCH; exponential smoothing; solar irradiation; solar farm; wind farm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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