A hybrid model for GEFCom2014 probabilistic electricity price forecasting

B-Tier
Journal: International Journal of Forecasting
Year: 2016
Volume: 32
Issue: 3
Pages: 1051-1056

Score contribution per author:

1.005 = (α=2.01 / 2 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This paper provides detailed information on Team Poland’s winning methodology in the electricity price forecasting track of GEFCom2014. A new hybrid model extending the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2015) is proposed. It consists of four major blocks: point forecasting, pre-filtering, quantile regression modeling and post-processing. This universal model structure enables a single block to be developed independently, without the performances of the remaining blocks being affected. The four-block model design is complemented by the inclusion of expert judgement, which may be of great importance in periods of unusually high or low electricity demand.

Technical Details

RePEc Handle
repec:eee:intfor:v:32:y:2016:i:3:p:1051-1056
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25