Probabilistic short-term load forecasting models based on a parametric approach |
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L.A. Fernández-Jiménez, S. Terreros-Olarte, C. Capellán-Villacián, A. Falces, E. García-Garrido, Department of Electrical Engineering, E.T.S.I.I., University of La Rioja,
2026-01-20
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Abstract
The forecasting of electric load plays an essential role in the effective management of electric power systems. Specifically, short-term models, which predict hourly load over the 24 hours of the subsequent day, hold significant value for applications within the realm of electricity markets. In this context, research efforts have predominantly concentrated on the development of point load forecasting models. These models provide solely the estimated value of hourly load, omitting any information regarding its associated uncertainty. Probabilistic forecasting models aim to address this limitation by offering comprehensive information on forecasted values, including their associated uncertainty, thereby enabling their more effective utilization in risky decision-making environments. This paper presents a parametric probabilistic model designed for hourly load forecasting. The model is refined through a multi-objective genetic algorithm optimization process that identifies explanatory variables from a specified set. The selected variables are combined linearly to predict the parameters of a probability distribution function for the hourly load. The process also selects the type of distribution from among those characterized by two parameters. The model is applied to data from a real distribution substation, yielding superior forecasting evaluation indexes compared to those achieved by two benchmark probabilistic load forecasting models.
Key words: Short-term load forecasting, probabilistic forecasting, multi-objective optimization, model calibration, uncertainty
Published in: Renewable Energies, Environment
& Power Quality Journal (REE&PQJ) |
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| ISSUE: Vol. 24. No. 3 | Pages: 356-361 |
| E-ISSN: 3020-531 X | Date of Current Version: 2026-01-02 |
| REF: 160 | Issue Date: 2026-01-26 |
| DOI:10.24084/reepqj24-160 | Publisher: AEDERMACP/ EA4EPQ |
References
[1] S. Akhtar, S. Shahzad, A. Zaheer, H.S. Ullah, H. Kilic, R. Gono, M. Jasiński and Z. Leonowicz, “Short-Term Load
Forecasting Models: A Review of Challenges, Progress, and the Road Ahead”, Energies (2023). Vol. 16(10), 4060.
[2] M. López, S. Valero, C. Senabre, J. Aparicio and A. Gabaldon, “Application of SOM neural networks to shortterm
load forecasting: The Spanish electricity market case study”, Electric Power Systems Research (2012). Vol. 91,
pp. 18–27.
[3] T. Hong and S. Fan, “Probabilistic electric load forecasting: A tutorial review”, International Journal of Forecasting
(2016). Vol. 32(3), pp. 914–38.
[4] A. Bracale, P. Caramia, P. de Falco and T. Hong, “Multivariate Quantile Regression for Short-Term Probabilistic Load Forecasting”, IEEE Trans. Power Syst. (2020). Vol. 35(1), pp. 628–638.
[5] P. Pinson, H.A. Nielsen, J.K. Møller, H. Madsen and G.N. Kariniotakis, “Non‐parametric probabilistic forecasts of
wind power: required properties and evaluation,” Wind Energy (2007). Vol. 10(6), pp. 497–516.
[6] T. Gneiting, S. Lerch and B. Schulz, “Probabilistic solar forecasting: Benchmarks, post-processing, verification”,
Solar Energy (2023). Vol. 252, pp. 72–80.
[7] M.D. Stasinopoulos, R.A. Rigby, G.Z. Heller, V. Voudouris and F. De Bastiani, Flexible Regression and Smoothing.
Using GAMLSS in R. CRC Press. Boca Raton (2017).
[8] P. Lauret, M. David, and P. Pinson, “Verification of solar irradiance probabilistic forecasts”, Solar Energy (2019). Vol.
194, pp. 254–271.
[9] M. Zamo and P. Naveau, “Estimation of the Continuous Ranked Probability Score with Limited Information and
Applications to Ensemble Weather Forecasts,” Mathematical Geosciences (2018). Vol. 50(2), pp. 209–234.
[10] M. Gebetsberger, J.W. Messner, G.J. Mayr and A. Zeileis, “Estimation methods for nonhomogeneous regression
models: Minimum continuous ranked probability score versus maximum likelihood”. Monthly Weather Review (2018). Vol. 146(12), pp. 4323–4338.
[11] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”,
IEEE Transactions on Evolutionary Computation (2002). Vol. 6(2), pp. 182–197.
[12] R. Koenker and G. Bassett, “Regression quantiles”, Econometrica(1978). Vol. 46, pp. 33–50.
[13] D.S. Wilks, “Indices of rank histogram flatness and their sampling properties”, Monthly Weather Review (2019). Vol.
147(2), pp. 763–769.
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