New probabilistic price forecasting models: Application to the Iberian electricity market

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Cláudio Monteiro (Author) FEUP; Ignacio J. Ramirez-Rosado (Author) Other; L. Alfredo Fernandez-Jimenez (Author) Other; Miguel Ribeiro (Author) Other

Journal

Title: International Journal of Electrical Power & Energy Systems

Vol. 103

Pages: 483-496

ISSN: 0142-0615

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 26 Citations

Scopus

INSPEC

Other information

Authenticus ID: P-00P-D9R

DOI: 10.1016/j.ijepes.2018.06.005

Abstract (EN): This article presents original Probabilistic Price Forecasting Models, for day-ahead hourly price forecasts in electricity markets, based on a Nadaraya-Watson Kernel Density Estimator approach. A Gaussian Kernel Density Estimator function is used for each input variable, which allows to calculate the parameters of the probability density function (PDF) of a Beta distribution for the hourly price variable. Thus, valuable information is obtained from PDFs such as point forecasts, variance values, quantiles, probabilities of prices, and time series representations of forecast uncertainty. A Reliability Indicator is also introduced to give a measure of "reliability" of forecasts. The Probabilistic Price Forecasting Models were satisfactorily applied to the real-world case study of the Iberian Electricity Market. Input variables of these models include recent prices, power demands and power generations in the previous day, power demands in the previous week, forecasts of demand, wind power generation and weather for the day-ahead, and chronological data. The best model, corresponding to the best combination of input variables that achieves the lowest MAE, obtains one of the highest Reliability Indicator values. A systematic analysis of MAE values of the Probabilistic Price Forecasting Models for different combinations of input variables showed that as more types of input variables were considered in these models, MAE values improved and Reliability Indicator values usually increased.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14