Forecasting quantiles of day-ahead electricity load

Z. Li, Stan Hurn and Adam Clements

Energy Economics, 2017, vol. 67, issue C, 60-71

Abstract: Accurate load forecasting plays a crucial role in the decision making process of many market participants, but probably is most important for the dispatch planning of an electricity market operator. Despite the competitive forecast accuracy achieved by existing point forecast models, point forecasts can only provide limited information relating to the expected level of future load. To account for the uncertainty of future load, and provide a more complete picture of the future load conditions for dispatch planning purposes, quantile forecasts can be useful. This paper proposes a computationally efficient approach to forecasting the quantiles of electricity load, which is then applied to forecasting in the National Electricity Market of Australia. The proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods. One of the main advantages of the proposed approach is the ease with the number of covariates can be expanded. This is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts.

Keywords: Load forecasting; Quantile forecasts; Bayesian quantile regression (search for similar items in EconPapers)
JEL-codes: Q41 Q48 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:67:y:2017:i:c:p:60-71

DOI: 10.1016/j.eneco.2017.08.002

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