Multiple split approach – multidimensional probabilistic forecasting of electricity markets

ArXiv ID: 2407.07795 “View on arXiv”

Authors: Unknown

Abstract

In this article, a multiple split method is proposed that enables construction of multidimensional probabilistic forecasts of a selected set of variables. The method uses repeated resampling to estimate uncertainty of simultaneous multivariate predictions. This nonparametric approach links the gap between point and probabilistic predictions and can be combined with different point forecasting methods. The performance of the method is evaluated with data describing the German short-term electricity market. The results show that the proposed approach provides highly accurate predictions. The gains from multidimensional forecasting are the largest when functions of variables, such as price spread or residual load, are considered. Finally, the method is used to support a decision process of a moderate generation utility that produces electricity from wind energy and sells it on either a day-ahead or an intraday market. The company makes decisions under high uncertainty because it knows neither the future production level nor the prices. We show that joint forecasting of both market prices and fundamentals can be used to predict the distribution of a profit, and hence helps to design a strategy that balances a level of income and a trading risk.

Keywords: multidimensional probabilistic forecasts, nonparametric method, resampling, electricity market, profit distribution, Commodities (Electricity)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces a nonparametric resampling method (multiple split approach) requiring significant statistical reasoning and extension to multivariate distributions, but the rigorous evaluation on German electricity market data with established metrics (CRPS, coverage) and a practical trading strategy application demonstrates high empirical implementation depth.

  flowchart TD
    A["Research Goal:<br>Multidimensional Probabilistic<br>Forecasting of Electricity Markets"] --> B["Data: German Short-Term<br>Electricity Market Data"]
    
    B --> C{"Methodology:<br>Multiple Split Approach"}
    
    C --> D["Computational Process:<br>Repeated Resampling<br>Nonparametric Uncertainty Estimation"]
    
    D --> E["Key Findings / Outcomes"]
    
    E --> F["Highly Accurate<br>Multivariate Predictions<br>of Prices & Fundamentals"]
    
    E --> G["Profit Distribution Prediction<br>for Wind Energy Generator<br>Optimizing Day-Ahead vs<br>Intraday Market Decisions"]
    
    E --> H["Enhanced Value from<br>Functions of Variables<br>e.g., Price Spread, Residual Load"]