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.
Highlights
The dynamic nature of most of the natural or artificial systems and the human necessity to ensure their correct performance in the future have led to the development of forecasting techniques.A decision maker needs forecasts of his/her variables of interest if there is uncertainty about their future values
In spite of a better use of the limited number of forecast days of the competition, in this article we evaluate the performance limited of predictors for the 14 days of competition, such competition, andarticle maximizing thethe utilization of the available number of forecast days of the in this we evaluate performance of predictors information
PPFMCP models were applied to the real-life case of the MIBEL
Summary
The dynamic nature of most of the natural or artificial systems and the human necessity to ensure their correct performance in the future have led to the development of forecasting techniques. This article describes original probabilistic price forecasting models (PPFMCP models) by aggregation of competitive predictors for probabilistic forecasts of the day-ahead hourly price and the provision of PIs by using a PDF of a Beta distribution These PPFMCP models were satisfactorily applied to the Iberian Electricity Market (Mercado Ibérico de Electricidad—MIBEL). In EEM2016 EPF competition an extensive set of available data for the predictors was provided to the participants This data included hourly prices in the previous days, forecasts of hourly demand and wind power generation for the Spanish area, weather forecasts (hourly wind speed, wind direction, precipitation, temperature and irradiation) for the day-ahead in the region PPFMCP models and practical probabilistic information from their PDFs for hourly price forecasts can be useful for MIBEL agents of the day-ahead market as well as for other agents of the power industry.
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