Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market
The volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predicto...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838567/ |
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| author | Yuki Osone Daisuke Kodaira |
| author_facet | Yuki Osone Daisuke Kodaira |
| author_sort | Yuki Osone |
| collection | DOAJ |
| description | The volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predictors, focusing on the UK market. Unlike market-specific models, this method ensures adaptability and reduces complexity. Using 1,132 days of training data, including electricity prices, demand forecasts, and generation forecasts obtained from UK electricity companies, results show that the proposed model achieved a mean absolute error of 18.27 [(£/MWh] for predicting volatile short-term spot market prices. The QR model achieved high predictive accuracy and stability, with only a 4–25% average pinball loss increases when the previous day’s prices (<inline-formula> <tex-math notation="LaTeX">$P_{t-1}$ </tex-math></inline-formula>) were excluded due to bidding deadlines. These findings demonstrate the model’s robustness and its potential to enhance market efficiency by providing reliable and simplified probabilistic forecasts, aiding stakeholders in mitigating risks and optimizing strategies. |
| format | Article |
| id | doaj-art-36034b70dcaf42c8a7933af102962ae9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36034b70dcaf42c8a7933af102962ae92025-01-21T00:01:48ZengIEEEIEEE Access2169-35362025-01-0113100831009310.1109/ACCESS.2025.352845010838567Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity MarketYuki Osone0https://orcid.org/0009-0006-5963-6224Daisuke Kodaira1https://orcid.org/0000-0003-0065-9203Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, JapanInstitute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, JapanThe volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predictors, focusing on the UK market. Unlike market-specific models, this method ensures adaptability and reduces complexity. Using 1,132 days of training data, including electricity prices, demand forecasts, and generation forecasts obtained from UK electricity companies, results show that the proposed model achieved a mean absolute error of 18.27 [(£/MWh] for predicting volatile short-term spot market prices. The QR model achieved high predictive accuracy and stability, with only a 4–25% average pinball loss increases when the previous day’s prices (<inline-formula> <tex-math notation="LaTeX">$P_{t-1}$ </tex-math></inline-formula>) were excluded due to bidding deadlines. These findings demonstrate the model’s robustness and its potential to enhance market efficiency by providing reliable and simplified probabilistic forecasts, aiding stakeholders in mitigating risks and optimizing strategies.https://ieeexplore.ieee.org/document/10838567/Electricity pricequantile regressionprobabilistic forecastingday-ahead marketrenewable energypinball loss |
| spellingShingle | Yuki Osone Daisuke Kodaira Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market IEEE Access Electricity price quantile regression probabilistic forecasting day-ahead market renewable energy pinball loss |
| title | Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market |
| title_full | Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market |
| title_fullStr | Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market |
| title_full_unstemmed | Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market |
| title_short | Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market |
| title_sort | quantile regression for probabilistic electricity price forecasting in the u k electricity market |
| topic | Electricity price quantile regression probabilistic forecasting day-ahead market renewable energy pinball loss |
| url | https://ieeexplore.ieee.org/document/10838567/ |
| work_keys_str_mv | AT yukiosone quantileregressionforprobabilisticelectricitypriceforecastingintheukelectricitymarket AT daisukekodaira quantileregressionforprobabilisticelectricitypriceforecastingintheukelectricitymarket |