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Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

Bidong Liu, Jakub Nowotarski, Tao Hong and Rafał Weron ()

No HSC/15/01, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Technology

Abstract: Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing Quantile Regression Averaging (QRA) on a set of sister point forecasts. There are two major benefits of the proposed approach: 1) it can leverage the development in the point load forecasting literature over the past several decades; and 2) it does not rely so much on high quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Comparing with the benchmark methods that utilize the variability of a selected individual forecast, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.

Keywords: Electric load forecasting; Forecast combination; Pinball loss function; Probabilistic forecasting; Prediction interval; Quantile regression; Sister forecast; Winkler score (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for
Date: 2015-02-03
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Forthcoming in IEEE Transactions on Smart Grid (doi: 10.1109/TSG.2015.2437877), 2016.

Downloads: (external link) Revised version, 2015-03-26 (application/pdf)

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