Generative Price-Path Forecasts for Intraday Electricity Trading

Summary

The paper presents a generative neural network for probabilistic forecasts of intraday electricity prices in Germany’s continuous-time market. Instead of producing only a point estimate, the method generates possible price paths and uses them to inform market sell-order placement in a fixed-volume trading setting.

The authors report that the model performs competitively against two statistical forecasting benchmarks on statistical metrics. In their economic evaluation, strategies based on its generated paths produce larger profit gains than strategies based on the benchmarks. The excerpt does not specify the model architecture, precise order rules, evaluation period, transaction-cost treatment, or magnitude of the gains. Results are specific to the German intraday market and the stated scenario, so they do not establish that the approach generalizes to other markets or trading conditions.

Key ideas

• The forecasting model generates probabilistic paths for intraday electricity prices.
• The generated paths are used to guide sell orders in a fixed-volume trading scenario.
• Statistical performance is compared with two benchmark forecasting methods.
• The reported trading gains favor the generative model, but the excerpt omits details needed to assess costs and generalizability.

Tags

# Probabilistic intraday electricity price forecasting using generative machine learning


# Probabilistic intraday electricity price forecasting using generative machine learning









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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