An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/8/9/10293 |
| _version_ | 1818015001731923968 |
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| author | Antonio Bracale Pasquale De Falco |
| author_facet | Antonio Bracale Pasquale De Falco |
| author_sort | Antonio Bracale |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:52:19Z |
| format | Article |
| id | doaj.art-51f30b622d0d4cdc95b8c421188e188d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T06:52:19Z |
| publishDate | 2015-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-51f30b622d0d4cdc95b8c421188e188d2022-12-22T02:07:00ZengMDPI AGEnergies1996-10732015-09-0189102931031410.3390/en80910293en80910293An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind PowerAntonio Bracale0Pasquale De Falco1Department of Engineering, University of Naples Parthenope, Centro Direzionale Is. C4, Naples 80143, ItalyDepartment of Electrical Engineering and Information Technologies, University of Naples Federico II Via Claudio 21, Naples 80125, ItalyCurrently, 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.http://www.mdpi.com/1996-1073/8/9/10293wind energypower productionforecasting methodsprobabilistic approaches |
| spellingShingle | Antonio Bracale Pasquale De Falco An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power Energies wind energy power production forecasting methods probabilistic approaches |
| title | An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power |
| title_full | An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power |
| title_fullStr | An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power |
| title_full_unstemmed | An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power |
| title_short | An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power |
| title_sort | advanced bayesian method for short term probabilistic forecasting of the generation of wind power |
| topic | wind energy power production forecasting methods probabilistic approaches |
| url | http://www.mdpi.com/1996-1073/8/9/10293 |
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