mlsanityData to 14 Oct 2026

Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market

arXiv 2502.04935 ↗First posted 7 Feb 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 62 references that are in the map. First posted on arXiv 7 Feb 2025.

Builds on

14 of 62 references.

References · 14

  1. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  2. 2013Learning by Transductioncited by 121
  3. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
  4. 2023Conformal PID Control for Time Series Predictioncited by 88
  5. 2022Sequential Predictive Conformal Inference for Time Seriescited by 75
  6. 2021Valid prediction intervals for regression problemscited by 29
  7. 2022Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecastingcited by 27
  8. 2022Distributional neural networks for electricity price forecastingcited by 24
  9. 2019Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Marketscited by 19
  10. 2020Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Pricescited by 11
  11. 2022Probabilistic forecasting of German electricity imbalance pricescited by 10
  12. 2021Probabilistic Forecasting of Imbalance Prices in the Belgian Contextcited by 8
2 more
  1. 2024Electricity Price Forecasting in the Irish Balancing Marketcited by 6
  2. 2023Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Marketscited by 0
  2. 2025Recurrent Neural Networks with Linear Structures for Electricity Price Forecastingcited by 0
  3. 2026Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service marketscited by 0