Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

Antonella R. Finamore, Vito Calderaro (), Vincenzo Galdi, Giuseppe Graber, Lucio Ippolito and Gaspare Conio
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Antonella R. Finamore: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
Vito Calderaro: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
Vincenzo Galdi: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
Giuseppe Graber: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
Lucio Ippolito: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy
Gaspare Conio: EOS Consulting SpA, 00144 Rome, Italy

Energies, 2023, vol. 16, issue 22, 1-14

Abstract: This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

Keywords: wind power generation; wind speed forecasting; artificial neural network; machine learning; clustering algorithm; particle swarm optimization; mesoscale data (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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