Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts

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.