An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power

Energies

Antonio Bracale¹  Pasquale De Falco² 

[1] Department of Engineering, University of Naples Parthenope, Centro Direzionale Is. C4, Naples 80143, Italy; E-Mail:;Department of Electrical Engineering and Information Technologies, University of Naples Federico II Via Claudio 21, Naples 80125, Italy

关键词: wind energy;    power production;    forecasting methods;    probabilistic approaches;   

DOI  :  10.3390/en80910293

来源: mdpi

【 摘 要 】

Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.