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Energies 2018, 11(5), 1074; https://doi.org/10.3390/en11051074

Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

1
Department of Electrical and Computer Engineering, Faculty of Engineering of the University of Porto (FEUP), 4200-465 Porto, Portugal
2
Electrical Engineering Department, University of Zaragoza, 50018 Zaragoza, Spain
3
Electrical Engineering Department, University of La Rioja, 26004 Logroño, Spain
*
Author to whom correspondence should be addressed.
Received: 8 March 2018 / Revised: 21 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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Abstract

This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made. View Full-Text
Keywords: electricity price forecasting; probabilistic forecast; electricity market prices; Iberian electricity market (MIBEL); daily session prices electricity price forecasting; probabilistic forecast; electricity market prices; Iberian electricity market (MIBEL); daily session prices
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Monteiro, C.; Ramirez-Rosado, I.J.; Fernandez-Jimenez, L.A. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors. Energies 2018, 11, 1074.

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