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

Fabio Famoso, Ludovica Maria Oliveri, Sebastian Brusca and Ferdinando Chiacchio ()
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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

Energies, 2024, vol. 17, issue 7, 1-24

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.

Keywords: cluster analysis; artificial intelligence algorithms; Reliability Block Diagrams; wind energy; wind farm production estimation; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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