Multiple split approach -- multidimensional probabilistic forecasting of electricity markets

TL;DR

This paper introduces a nonparametric multiple split method for multidimensional probabilistic forecasting in electricity markets, improving prediction accuracy and supporting risk-aware decision-making.

Contribution

It proposes a novel multiple split approach that estimates uncertainty in multivariate forecasts and demonstrates its effectiveness on real electricity market data.

Findings

The method yields highly accurate multidimensional predictions.

Forecasting functions of variables enhances prediction gains.

Joint forecasts of prices and fundamentals aid profit distribution analysis.

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.