Probabilistic intraday electricity price forecasting using generative machine learning

TL;DR

This paper introduces a novel generative neural network model for probabilistic intraday electricity price forecasting, demonstrating improved trading strategies and economic gains in Germany’s intraday market.

Contribution

The paper presents a new generative machine learning approach for probabilistic price path forecasting and its application to trading strategies, outperforming existing benchmarks.

Findings

The generative model achieves competitive statistical performance.

Trading strategies based on generated paths yield higher profits.

The approach demonstrates economic value in intraday electricity trading.

Abstract

The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation.