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Combining predictive distributions of electricity prices : does minimizing the CRPS lead to optimal decisions in day-ahead bidding?

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
Rocznik
Strony
105--118
Opis fizyczny
Bibliogr. 31 poz., rys.
Twórcy
  • Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland
autor
  • Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland
Bibliografia
  • [1] Berrisch, J., and Ziel, F. CRPS learning. Journal of Econometrics (2021), 105221. DOI: 10.1016/j.jeconom.2021.11.008.
  • [2] Berrisch, J., and Ziel, F. Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices, 2023. DOI: 10.48550/arXiv.2303.10019. Working paper version available from arXiv: https://arxiv.org/abs/2303.10019.
  • [3] Berrisch, J., and Ziel, F. The profoc Package: An R package for probabilistic forecast combination using CRPS Learning, 2023. R package version 1.2.0.
  • [4] Blanc, S. M., and Setzer, T. Bias–variance trade-off and shrinkage of weights in forecast combination. Management Science 66, 12 (2020), 5720–5737.
  • [5] Diebold, F. X., and Mariano, R. S. Comparing predictive accuracy. Journal of Business & Economic Statistics 13, 3 (1995), 253–263.
  • [6] Gneiting, T. Making and evaluating point forecasts. Journal of the American Statistical Association 106, 494 (2011), 746–762.
  • [7] Gneiting, T., and Katzfuss, M. Probabilistic forecasting. The Annual Review of Statistics and Its Application 1 (2014), 125–151.
  • [8] Gneiting, T., and Raftery, A. E. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102, 477 (2007), 359–378.
  • [9] Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., and Hyndman, R. J. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting 32, 3 (2016), 896–913.
  • [10] Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., and Zareipour, H. Energy forecasting: A review and outlook. IEEE Open Access Journal of Power and Energy 7 (2020), 376–388.
  • [11] Hubicka, K., Marcjasz, G., and Weron, R. A note on averaging day-ahead electricity price forecasts across calibration windows. IEEE Transactions on Sustainable Energy 10, 1 (2019), 321–323.
  • [12] Janczura, J., and Puć, A. ARX-GARCH probabilistic price forecasts for diversification of trade in electricity markets—variance stabilizing transformation and financial risk-minimizing portfolio allocation. Energies 16, 2 (2023).
  • [13] Janczura, J., and Wójcik, E. Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study. Energy Economics 110 (2022), 106015.
  • [14] Lago, J., Marcjasz, G., De Schutter, B., and Weron, R. Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy 293 (2021), 116983.
  • [15] Lichtendahl, K. C., Grushka-Cockayne, Y., and Winkler, R. L. Is it better to average probabilities or quantiles? Management Science 59, 7 (2013), 1594–1611.
  • [16] Maciejowska, K. Portfolio management of a small RES utility with a structural vector autoregressive model of electricity markets in Germany. Operations Research and Decisions 32, 4 (2022), 75-90.
  • [17] Maciejowska, K., Nitka, W., and Weron, T. Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices. Energy Economics 99 (2021), 105273.
  • [18] Maciejowska, K., Uniejewski, B., and Weron, R. Forecasting electricity prices. In Oxford Research Encyclopedia of Economics and Finance. Oxford University Press, 2023. DOI: 10.1093/acrefore/9780190625979.013.667. Working paper version available from arXiv: https://doi.org/10.48550/arXiv.2204.11735.
  • [19] Makridakis, S., Spiliotis, E., and Assimakopoulos, V. The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34, 4 (2018), 802–808.
  • [20] Marcjasz, G., Narajewski, M., Weron, R., and Ziel, F. Distributional neural networks for electricity price forecasting. Energy Economics 125 (2023), 106843.
  • [21] Marcjasz, G., Uniejewski, B., and Weron, R. Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? International Journal of Forecasting 36, 2 (2020), 466–479.
  • [22] Nowotarski, J., and Weron, R. Computing electricity spot price prediction intervals using quantile regression and forecast averaging. Computational Statistics 30, 3 (2015), 791–803.
  • [23] Nowotarski, J., and Weron, R. Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews 81 (2018), 1548–1568.
  • [24] Timmermann, A. Forecast Combinations. In Handbook of Economic Forecasting, G. Elliott, C. W. J. Granger, and A. Timmermann, Eds., vol. 1. Elsevier, 2006, pp. 135–196.
  • [25] Uniejewski, B. Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices, 2023. DOI: 10.48550/arXiv.2302.00411. Working paper version available from arXiv: https://arxiv.org/abs/2302.00411.
  • [26] Uniejewski, B., and Maciejowska, K. Lasso principal component averaging: A fully automated approach for point forecast pooling. International Journal of Forecasting (2022). DOI: 10.1016/j.ijforecast.2022.09.004, (in press).
  • [27] Uniejewski, B., and Weron, R. Regularized quantile regression averaging for probabilistic electricity price forecasting. Energy Economics 95 (2021), 105121.
  • [28] Vogler, A., and Ziel, F. Event-based evaluation of electricity price ensemble forecasts. Forecasting 4, 1 (2022), 51–71.
  • [29] Wang, X., Hyndman, R. J., Li, F., and Kang, Y. Forecast combinations: An over 50-year review. International Journal of Forecasting (2022). DOI: 10.1016/j.ijforecast.2022.11.005, (in press).
  • [30] Weron, R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting 30, 4 (2014), 1030–1081.
  • [31] Yardley, E., and Petropoulos, F. Beyond error measures to the utility and cost of the forecasts. Foresight: The International Journal of Applied Forecasting 63 (2021), 36–45.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-64762331-6fd3-44da-9a51-cae65e0427f8
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