What does this research mean for the field?

Integrating probabilistic electricity price forecasts using LightGBM quantile regression into portfolio optimization strategies improves trading profits by up to 15% during high-volatility energy crisis periods compared to point-forecast benchmarks. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate a profit-maximization and risk-management strategy for electricity suppliers using probabilistic price forecasts during periods of market crisis and high volatility.

September 2, 2026International Journal of Green Energy

Probabilistic electricity price forecasting for profit maximization during energy crisis periods

View Full Paper

Ask AI

Bookmark

Authors

SGSalih Gündüz UUUmut Uğurlu İÖİlkay Öksüz

Discussion

Member takes

Overview

Simulation study demonstrates improved trading profits in European electricity markets using probabilistic forecasting, highlighting the value of risk-aware portfolio optimization.

Key Points

To evaluate a profit-maximization and risk-management strategy for electricity suppliers using probabilistic price forecasts during periods of market crisis and high volatility.
Trained a LightGBM quantile regression model (LightGBM-QR) to generate probabilistic day-ahead electricity price forecasts, evaluating accuracy using the Winkler score on data up to 2024.
Simulated an electricity supplier trading across hourly contracts in German, French, and Iberian day-ahead markets using portfolio optimization targeting risk minimization, profit maximization, and Sharpe ratio maximization.
Over the two-year test window, probabilistic strategies improved trading profits by approximately 6% in Germany, 5% in France, and 4% in Iberia compared to point-forecast benchmarks.
During the peak of the energy crisis in late 2021, the strategy raised profits by 15% in Germany, 9% in France, and 10% in the Iberian market relative to the benchmark.

Cite This Study

Gündüz et al. (2026) studied this question.

synapsesocial.com/papers/6a97e29ec562ede874ec6d5c https://doi.org/10.1080/15435075.2026.2720512

View Full Paper

Ask AI

Bookmark

September 2, 2026International Journal of Green Energy

Probabilistic electricity price forecasting for profit maximization during energy crisis periods

View Full Paper

Ask AI

Bookmark

Authors

SGSalih Gündüz UUUmut Uğurlu İÖİlkay Öksüz

Discussion

Member takes

Overview

Simulation study demonstrates improved trading profits in European electricity markets using probabilistic forecasting, highlighting the value of risk-aware portfolio optimization.

Key Points

To evaluate a profit-maximization and risk-management strategy for electricity suppliers using probabilistic price forecasts during periods of market crisis and high volatility.
Trained a LightGBM quantile regression model (LightGBM-QR) to generate probabilistic day-ahead electricity price forecasts, evaluating accuracy using the Winkler score on data up to 2024.
Simulated an electricity supplier trading across hourly contracts in German, French, and Iberian day-ahead markets using portfolio optimization targeting risk minimization, profit maximization, and Sharpe ratio maximization.
Over the two-year test window, probabilistic strategies improved trading profits by approximately 6% in Germany, 5% in France, and 4% in Iberia compared to point-forecast benchmarks.
During the peak of the energy crisis in late 2021, the strategy raised profits by 15% in Germany, 9% in France, and 10% in the Iberian market relative to the benchmark.

Cite This Study

Gündüz et al. (2026) studied this question.

synapsesocial.com/papers/6a97e29ec562ede874ec6d5c https://doi.org/10.1080/15435075.2026.2720512

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark