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

arXiv: https://arxiv.org/abs/2502.04935 · HTML: https://mlsanity.com/paper/2502.04935/

## 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 (most cited first, 14 of 14)

- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [Learning by Transduction](https://mlsanity.com/paper/1301.7375/) (2013)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Conformal PID Control for Time Series Prediction](https://mlsanity.com/paper/2307.16895/) (2023)
- [Sequential Predictive Conformal Inference for Time Series](https://mlsanity.com/paper/2212.03463/) (2022)
- [Valid prediction intervals for regression problems](https://mlsanity.com/paper/2107.00363/) (2021)
- [Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2202.08756/) (2022)
- [Distributional neural networks for electricity price forecasting](https://mlsanity.com/paper/2207.02832/) (2022)
- [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://mlsanity.com/paper/1905.07886/) (2019)
- [Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices](https://mlsanity.com/paper/2010.01844/) (2020)
- [Probabilistic forecasting of German electricity imbalance prices](https://mlsanity.com/paper/2205.11439/) (2022)
- [Probabilistic Forecasting of Imbalance Prices in the Belgian Context](https://mlsanity.com/paper/2106.07361/) (2021)
- [Electricity Price Forecasting in the Irish Balancing Market](https://mlsanity.com/paper/2402.06714/) (2024)
- [Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs](https://mlsanity.com/paper/2310.03339/) (2023)

## Built on by (most cited first, 3 of 3)

- [The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets](https://mlsanity.com/paper/2511.05523/) (2025)
- [Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting](https://mlsanity.com/paper/2512.04690/) (2025)
- [Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets](https://mlsanity.com/paper/2601.14663/) (2026)

## 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.