Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts

Tao Hong, Katarzyna Maciejowska (), Jakub Nowotarski and Rafał Weron

No HSC/14/10, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Probabilistic load forecasting is becoming crucial in today's power systems planning and operations. We propose a novel methodology to compute interval forecasts of electricity demand, which applies a Quantile Regression Averaging (QRA) technique to a set of independent expert point forecasts. We demonstrate the effectiveness of the proposed methodology using data from the hierarchical load forecasting track of the Global Energy Forecasting Competition 2012. The results show that the new method is able to provide better prediction intervals than four benchmark models for the majority of the load zones and the aggregated level.

Keywords: Electric load; Probabilistic forecasting; Prediction interval; Quantile regression; Forecasts combination; Expert forecast (search for similar items in EconPapers)
JEL-codes: C22 C32 C38 C53 Q47 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2014-07-15
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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