Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting
Abstract Accurate prediction of solar photovoltaic plant energy generation is essential for optimal planning and operation of modern power systems, and incorporating such plants into the energy sector. In this study, an adaptive Gaussian mixture method (AGM) and a developed variational Bayesian mode...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-09-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12194 |
| _version_ | 1798035749110546432 |
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| author | Oveis Abedinia Mehdi Bagheri Vassilios G. Agelidis |
| author_facet | Oveis Abedinia Mehdi Bagheri Vassilios G. Agelidis |
| author_sort | Oveis Abedinia |
| collection | DOAJ |
| description | Abstract Accurate prediction of solar photovoltaic plant energy generation is essential for optimal planning and operation of modern power systems, and incorporating such plants into the energy sector. In this study, an adaptive Gaussian mixture method (AGM) and a developed variational Bayesian model (VBM) inference through multikernel regression (MkR) are utilized to assist desirable precise prediction. In this model, the MkR processes the multiresolution solar energy signal, and then the AGM models the complex signals forecasting error. Finally, the proposed model can be optimized, and the concurrent output of the solar energy signal in both probabilistic and deterministic status can be attained through the introduction of the VBM. The solar energy output of an actual plant, including four measurement sites provided the data for the study. The results confirmed that the proposed model delivers higher prediction accuracy for both probabilistic and deterministic forecasts when compared with other well‐known models. |
| first_indexed | 2024-04-11T21:02:32Z |
| format | Article |
| id | doaj.art-05b4e270ead649ae97eabf78389c4e2d |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T21:02:32Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-05b4e270ead649ae97eabf78389c4e2d2022-12-22T04:03:27ZengWileyIET Renewable Power Generation1752-14161752-14242021-09-0115122699271410.1049/rpg2.12194Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecastingOveis Abedinia0Mehdi Bagheri1Vassilios G. Agelidis2Department of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Kabanbay Batyr Ave. 53, Nur‐Sultan 010000 Kazakhstan Astana KazakhstanDepartment of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Kabanbay Batyr Ave. 53, Nur‐Sultan 010000 Kazakhstan Astana KazakhstanCentre for Electrical Power and Energy (CEE) Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby DenmarkAbstract Accurate prediction of solar photovoltaic plant energy generation is essential for optimal planning and operation of modern power systems, and incorporating such plants into the energy sector. In this study, an adaptive Gaussian mixture method (AGM) and a developed variational Bayesian model (VBM) inference through multikernel regression (MkR) are utilized to assist desirable precise prediction. In this model, the MkR processes the multiresolution solar energy signal, and then the AGM models the complex signals forecasting error. Finally, the proposed model can be optimized, and the concurrent output of the solar energy signal in both probabilistic and deterministic status can be attained through the introduction of the VBM. The solar energy output of an actual plant, including four measurement sites provided the data for the study. The results confirmed that the proposed model delivers higher prediction accuracy for both probabilistic and deterministic forecasts when compared with other well‐known models.https://doi.org/10.1049/rpg2.12194Solar power stations and photovoltaic power systemsPower system planning and layoutPower system measurement and meteringEnergy resourcesRegression analysis |
| spellingShingle | Oveis Abedinia Mehdi Bagheri Vassilios G. Agelidis Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting IET Renewable Power Generation Solar power stations and photovoltaic power systems Power system planning and layout Power system measurement and metering Energy resources Regression analysis |
| title | Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting |
| title_full | Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting |
| title_fullStr | Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting |
| title_full_unstemmed | Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting |
| title_short | Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting |
| title_sort | application of an adaptive bayesian based model for probabilistic and deterministic pv forecasting |
| topic | Solar power stations and photovoltaic power systems Power system planning and layout Power system measurement and metering Energy resources Regression analysis |
| url | https://doi.org/10.1049/rpg2.12194 |
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