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A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Author

Listed:
  • Fabio Famoso

    (Department of Engineering, University of Messina, 98166 Messina, Italy)

  • Ludovica Maria Oliveri

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy)

  • Sebastian Brusca

    (Department of Engineering, University of Messina, 98166 Messina, Italy)

  • Ferdinando Chiacchio

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy)

Abstract

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.

Suggested Citation

  • Fabio Famoso & Ludovica Maria Oliveri & Sebastian Brusca & Ferdinando Chiacchio, 2024. "A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant," Energies, MDPI, vol. 17(7), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1627-:d:1365928
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    References listed on IDEAS

    as
    1. Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    2. Kubik, M.L. & Coker, P.J. & Barlow, J.F. & Hunt, C., 2013. "A study into the accuracy of using meteorological wind data to estimate turbine generation output," Renewable Energy, Elsevier, vol. 51(C), pages 153-158.
    3. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    4. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    5. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    6. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    7. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
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    Cited by:

    1. Yan Yan & Yong Qian & Yan Zhou, 2025. "Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC," Energies, MDPI, vol. 18(7), pages 1-18, March.
    2. Spyros Giannelos, 2025. "Reinforcement Learning in Energy Finance: A Comprehensive Review," Energies, MDPI, vol. 18(11), pages 1-41, May.
    3. Tadashi Hosoe & Kazuto Yukita, 2024. "A Study on Wind Collection Effect of Vertical Axis Windmills," Energies, MDPI, vol. 17(23), pages 1-11, December.

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