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 examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to...

This paper provides detailed information on Team Poland’s approach in the electricity price forecasting track of GEFCom2014. A new hybrid model is proposed, consisting of four major blocks: point forecasting, pre-filtering, quantile regression modeling and post-processing. This universal model...

When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less...

When building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data. While the short-term seasonal components (daily, weekly) are more regular and less...

In this comprehensive empirical study we critically evaluate the use of forecast averaging in the context of electricity prices. We apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our...