Leveraging Continuous Trade Data for Intraday Imbalance Price Forecasting
Abstract
Forecasts of imbalance prices are essential for participants in short-term electricity markets. This study proposes an approach to integrate trade data from continuous intraday markets into forecasting methods and considers the production of both parametric and non-parametric density forecasts. Extensive feature engineering is required to convert irregular trade data into a suitable tabular structure. Our findings demonstrate that intraday trade data can enhance state-of-the-art forecasts based on quantile regression and LASSO. We also propose a model based on Natural Gradient Boosting to predict the means and variances of a bimodal Gaussian mixture, which offers both improved predictive performance and computational efficiency. In a case study based on multiple years of data from Great Britain, our proposed approach reduced the Pinball Loss of state-of-the-art models by 20% for forecasts made 30 seconds before gate closure, and by 7.5% for forecasts made one hour before delivery.
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BibTeX
@misc{stylpnopoulos2026leveraging,
title = {{Leveraging Continuous Trade Data for Intraday Imbalance Price Forecasting}},
author = {Klimis Stylpnopoulos and Jethro Browell and Janine Illian and Ciaran Gilbert and Wayne Jones},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03331},
eprint = {2611.03331},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03331},
}
RIS
TY - UNPB
TI - Leveraging Continuous Trade Data for Intraday Imbalance Price Forecasting
AU - Klimis Stylpnopoulos
AU - Jethro Browell
AU - Janine Illian
AU - Ciaran Gilbert
AU - Wayne Jones
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03331
UR - https://arxiv.org/abs/2611.03331
AB - Forecasts of imbalance prices are essential for participants in short-term electricity markets. This study proposes an approach to integrate trade data from continuous intraday markets into forecasting methods and considers the production of both parametric and non-parametric density forecasts. Extensive feature engineering is required to convert irregular trade data into a suitable tabular structure. Our findings demonstrate that intraday trade data can enhance state-of-the-art forecasts based on quantile regression and LASSO. We also propose a model based on Natural Gradient Boosting to predict the means and variances of a bimodal Gaussian mixture, which offers both improved predictive performance and computational efficiency. In a case study based on multiple years of data from Great Britain, our proposed approach reduced the Pinball Loss of state-of-the-art models by 20% for forecasts made 30 seconds before gate closure, and by 7.5% for forecasts made one hour before delivery.
ER -