Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading

Paper: arXiv 2303.08565

Authors: Katarzyna Maciejowska, Tomasz Serafin, Bartosz Uniejewski

Abstract

This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averaging scheme. The performance of the approach is evaluated on data sets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results show that the newly proposed method outperforms literature benchmarks in terms of statistical measures: the empirical coverage and the Christoffersen test for conditional coverage. Moreover, in line with recent literature trends, the economic value of forecasts is evaluated based on the trading strategy using probabilistic price predictions to optimize the operation of an energy storage system. The results suggest that apart from the use of statistical measures, there is a need for the economic evaluation of forecasts.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a solid hybrid Factor-QRA framework applied directly to real-world European electricity market data. It excels by pairing statistical validation (Christoffersen tests) with practical economic evaluations via energy storage trading strategies.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Electricity Price Probabilistic Forecasting & Trading Value] --> B[Data Inputs: German EPEX SPOT & Polish TGE Market Data]
    B --> C[Methodology: Combine Quantile Regression Averaging QRA & Factor-Based Averaging]
    C --> D[Computational Evaluation: Statistical Metrics & Energy Storage Optimization]
    D --> E[Statistical Outcomes: Outperforms Benchmarks in Empirical & Conditional Coverage]
    D --> F[Economic Outcomes: Optimized Storage Operations Confirm Real-World Trading Value]