Next Article in Journal
Inductive-Based Wireless Power Recharging System for an Innovative Endoscopic Capsule
Next Article in Special Issue
A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search
Previous Article in Journal
Determination of Priority Study Areas for Coupling CO2 Storage and CH4 Gas Hydrates Recovery in the Portuguese Offshore Area
Previous Article in Special Issue
The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation
Article Menu

Export Article

Open AccessArticle
Energies 2015, 8(9), 10293-10314;

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

Department of Engineering, University of Naples Parthenope, Centro Direzionale Is. C4, Naples 80143, Italy
Department of Electrical Engineering and Information Technologies, University of Naples Federico II Via Claudio 21, Naples 80125, Italy
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Enrico Sciubba
Received: 17 June 2015 / Revised: 3 August 2015 / Accepted: 11 September 2015 / Published: 21 September 2015
Full-Text   |   PDF [375 KB, uploaded 21 September 2015]   |  


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. View Full-Text
Keywords: wind energy; power production; forecasting methods; probabilistic approaches wind energy; power production; forecasting methods; probabilistic approaches

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Bracale, A.; De Falco, P. An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power. Energies 2015, 8, 10293-10314.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top