A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant
This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immedi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/7/1627 |
| _version_ | 1827286816680574976 |
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| author | Fabio Famoso Ludovica Maria Oliveri Sebastian Brusca Ferdinando Chiacchio |
| author_facet | Fabio Famoso Ludovica Maria Oliveri Sebastian Brusca Ferdinando Chiacchio |
| author_sort | Fabio Famoso |
| collection | DOAJ |
| description | This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. |
| first_indexed | 2024-04-24T10:45:17Z |
| format | Article |
| id | doaj.art-2eb506178458451b8a8d7f690dbeb1bf |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-24T10:45:17Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2eb506178458451b8a8d7f690dbeb1bf2024-04-12T13:17:54ZengMDPI AGEnergies1996-10732024-03-01177162710.3390/en17071627A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power PlantFabio Famoso0Ludovica Maria Oliveri1Sebastian Brusca2Ferdinando Chiacchio3Department of Engineering, University of Messina, 98166 Messina, ItalyDepartment of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, ItalyDepartment of Engineering, University of Messina, 98166 Messina, ItalyDepartment of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, ItalyThis paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.https://www.mdpi.com/1996-1073/17/7/1627cluster analysisartificial intelligence algorithmsReliability Block Diagramswind energywind farm production estimationartificial neural network |
| spellingShingle | Fabio Famoso Ludovica Maria Oliveri Sebastian Brusca Ferdinando Chiacchio A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant Energies cluster analysis artificial intelligence algorithms Reliability Block Diagrams wind energy wind farm production estimation artificial neural network |
| title | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant |
| title_full | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant |
| title_fullStr | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant |
| title_full_unstemmed | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant |
| title_short | A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant |
| title_sort | dependability neural network approach for short term production estimation of a wind power plant |
| topic | cluster analysis artificial intelligence algorithms Reliability Block Diagrams wind energy wind farm production estimation artificial neural network |
| url | https://www.mdpi.com/1996-1073/17/7/1627 |
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