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Conformal Prediction Techniques for Electricity Price Forecasting

  • Conference paper
  • First Online:
Advanced Analytics and Learning on Temporal Data (AALTD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15433))

  • 137 Accesses

Abstract

Integrating the erratic production of renewable energy into the electricity grid poses numerous challenges. One approach to stabilising market prices and reducing energy losses due to curtailments is the deployment of batteries. Efficient electricity arbitrage is crucial to make investments in storage systems financially viable; trading solutions to achieve this rely on price forecasting techniques. This study delves into the application of Conformal Prediction (CP) techniques, including Ensemble Batch Prediction Intervals (EnbPI) and Sequential Predictive Conformal Inference for Time Series (SPCI), for generating probabilistic forecasts in the Irish electricity market. Recent advancements in CP have addressed temporal considerations inherent in time series forecasting, eliminating the need for exchangeability assumptions. Our study demonstrates that despite potential efficiency trade-offs, CP methods consistently yield precise and reliable prediction intervals, ensuring comprehensive coverage. We assess the impact of CP on the financial results of a simulated trading algorithm. Monetary outcomes achieved with EnbPI and SPCI outperform those of both split CP and traditional quantile regression models, highlighting the practical superiority of CP in electricity price forecasting.

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Notes

  1. 1.

    https://www.sem-o.com/.

  2. 2.

    https://www.semopx.com/market-data/.

  3. 3.

    https://github.com/ciaranoc123/PEPF_Conformal.

References

  • Abramova, E., Bunn, D.: Optimal daily trading of battery operations using arbitrage spreads. Energies 14(16), 4931 (2021)

    Article  MATH  Google Scholar 

  • Dewolf, N., De Baets, B., Waegeman, W.: Valid prediction intervals for regression problems. Artif. Intell. Rev. 56(1), 577–613 (2023)

    Article  MATH  Google Scholar 

  • EirGrid. Renewable energy (2022). https://www.eirgridgroup.com/how-the-grid-works/renewables/

  • Barber, R.F., Candes, E.J., Ramdas, A., Tibshirani, R.J.: Conformal prediction beyond exchangeability. arXiv e-prints, pages arXiv–2202 (2022)

    Google Scholar 

  • Gammerman, A., Vovk, V., Vapnik, V.: Learning by transduction. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI) (1998)

    Google Scholar 

  • Ghosh, S., Shi, Y., Belkhouja, T., Yan, Y., Doppa, J., Jones, B.: Probabilistically robust conformal prediction. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), vol. 216, pp. 681–690 (2023)

    Google Scholar 

  • Giacomini, R., White, H.: Tests of conditional predictive ability. Econometrica 74(6), 1545–1578 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Green, R., Vasilakos, N.: Market behaviour with large amounts of intermittent generation. Energy Policy 38(7), 3211–3220 (2010)

    Article  MATH  Google Scholar 

  • Jianming, H., Luo, Q., Tang, J., Heng, J., Deng, Y.: Conformalized temporal convolutional quantile regression networks for wind power interval forecasting. Energy 248, 123497 (2022)

    Article  Google Scholar 

  • Jensen, V., Bianchi, F.M., Anfinsen, S.N.: Ensemble conformalized quantile regression for probabilistic time series forecasting. IEEE Trans. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  • Kath, C., Ziel, F.: Conformal prediction interval estimation and applications to day-ahead and intraday power markets. Int. J. Forecast. 37(2), 777–799 (2021)

    Article  MATH  Google Scholar 

  • Khajeh, H., Laaksonen, H.: Applications of probabilistic forecasting in smart grids: a review. Appl. Sci. 12(4), 1823 (2022)

    Article  MATH  Google Scholar 

  • Khosravi, A., Nahavandi, S.: Closure to the discussion of “prediction intervals for short-term wind farm generation forecasts’’ and “combined nonparametric prediction intervals for wind power generation’’ and the discussion of “combined nonparametric prediction intervals for wind power generation’’. IEEE Trans. Sustain. Energy 5(3), 1022–1023 (2014)

    Article  MATH  Google Scholar 

  • Krishnamurthy, D., Uckun, C., Zhou, Z., Thimmapuram, P.R., Botterud, A.: Energy storage arbitrage under day-ahead and real-time price uncertainty. IEEE Trans. Power Syst. 33(1), 84–93 (2017)

    Article  Google Scholar 

  • Narajewski, M.l., Ziel, F.: Optimal bidding on hourly and quarter-hourly day-ahead electricity price auctions: trading large volumes of power with market impact and transaction costs. arXiv preprint arXiv:2104.14204 (2021)

  • Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 221, 386–405 (2018)

    Article  Google Scholar 

  • Lago, J., Marcjasz, G., De Schutter, B., Weron, R.: Forecasting day-ahead electricity prices: a review of state-of-the-art algorithms, best practices and an open-access benchmark. Appl. Energy 293, 116983 (2021)

    Article  Google Scholar 

  • Leverger, C., et al.: Probabilistic forecasting of seasonal time series: combining clustering and classification for forecasting. In: International Conference on Time Series and Forecasting, pp. 47–63. Springer (2021)

    Google Scholar 

  • Maciejowska, K., Nowotarski, J., Weron, R.: Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. Int. J. Forecast. 32(3), 957–965 (2016)

    Article  MATH  Google Scholar 

  • Marcjasz, G., Uniejewski, B., Weron, R.: Probabilistic electricity price forecasting with narx networks: Combine point or probabilistic forecasts? Int. J. Forecast. 36(2), 466–479 (2020)

    Article  Google Scholar 

  • Marcjasz, G., Narajewski, M., Weron, R., Ziel, F.: Distributional neural networks for electricity price forecasting. arXiv preprint arXiv:2207.02832 (2022)

  • Martinez-Anido, C.B., Brinkman, G., Hodge, B.M.: The impact of wind power on electricity prices. Renew. Energy 94, 474–487 (2016)

    Article  MATH  Google Scholar 

  • Nowotarski, J., Weron, R.: Recent advances in electricity price forecasting: a review of probabilistic forecasting. Renew. Sustain. Energy Rev. 81, 1548–1568 (2018)

    Article  MATH  Google Scholar 

  • O’Connor, C., Collins, J., Prestwich, S., Visentin, A.: Electricity price forecasting in the irish balancing market. arXiv preprint arXiv:2402.06714 (2024a)

  • O’Connor, C., Collins, J., Prestwich, S., Visentin, A.: Optimizing quantile-based trading strategies in electricity arbitrage (2024b)

    Google Scholar 

  • Oesterheld, C., Treutlein, J., Cooper, E., Hudson, R.: Incentivizing honest performative predictions with proper scoring rules. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence, pp. 1564–1574 (2023)

    Google Scholar 

  • O’Connor, C., Collins, J., Prestwich, S., Visentin, A.: Electricity price forecasting in the irish balancing market. Energy Strategy Rev. 54, 101436 (2024). ISSN 2211-467X. https://doi.org/10.1016/j.esr.2024.101436. https://www.sciencedirect.com/science/article/pii/S2211467X24001433

  • Salem, T.S., Langseth, H., Ramampiaro, H.: Prediction intervals: Split normal mixture from quality-driven deep ensembles. In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 1179–1187 (2020)

    Google Scholar 

  • Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9(3) (2008)

    Google Scholar 

  • Staffell, I., Rustomji, M.: Maximising the value of electricity storage. J. Energy Storage 8, 212–225 (2016)

    Article  Google Scholar 

  • Tohidi, Y., Gibescu, M.: Stochastic optimisation for investment analysis of flow battery storage systems. IET Renew. Power Gener. 13(4), 555–562 (2019)

    Article  MATH  Google Scholar 

  • Tzallas, P., Bezas, N., Moschos, I., Ioannidis, D., Tzovaras, D.: Probabilistic quantile multi-step forecasting of energy market prices: a UK case study. In: Artificial Intelligence Applications and Innovations, pp. 301–313. Springer (2022)

    Google Scholar 

  • Uniejewski, B.: Smoothing quantile regression averaging: a new approach to probabilistic forecasting of electricity prices. arXiv preprint arXiv:2302.00411 (2023)

  • Uniejewski, B., Weron, R.: Regularized quantile regression averaging for probabilistic electricity price forecasting. Energy Econ. 95, 105121 (2021)

    Article  MATH  Google Scholar 

  • Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World, vol. 29. Springer (2005)

    Google Scholar 

  • Xu, C., Xie, Y.: Conformal prediction interval for dynamic time-series. In: International Conference on Machine Learning, pp. 11559–11569. PMLR (2021)

    Google Scholar 

  • Xu, C., Xie, Y.: Sequential predictive conformal inference for time series. In: International Conference on Machine Learning, pp. 38707–38727. PMLR (2023)

    Google Scholar 

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Author information

Authors and Affiliations

  1. SFI CRT in Artificial Intelligence, School of Computer Science & IT, University College Cork, Cork, Ireland

    Ciaran O’Connor

  2. Insight Centre for Data Analytics, School of Computer Science & IT, University College Cork, Cork, Ireland

    Steven Prestwich & Andrea Visentin

Authors
  1. Ciaran O’Connor
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  2. Steven Prestwich
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  3. Andrea Visentin
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Corresponding author

Correspondence to Ciaran O’Connor .

Editor information

Editors and Affiliations

  1. Orange Labs, Lannion, France

    Vincent Lemaire

  2. School of Computer Science, University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  3. School of Electronics and Computer Science, University of Southampton, Southampton, UK

    Anthony Bagnall

  4. Université Claude Bernard Lyon 1, Villeurbanne, France

    Thomas Guyet

  5. University of Rennes, Rennes Cedex, France

    Simon Malinowski

  6. Department of Computer Science, Humboldt University of Berlin, Berlin, Germany

    Patrick Schäfer

  7. Université de Rennes 2, Rennes, France

    Romain Tavenard

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O’Connor, C., Prestwich, S., Visentin, A. (2025). Conformal Prediction Techniques for Electricity Price Forecasting. In: Lemaire, V., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2024. Lecture Notes in Computer Science(), vol 15433. Springer, Cham. https://doi.org/10.1007/978-3-031-77066-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-77066-1_1

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