The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

Year 2023, , 213 - 223, 31.12.2023

Serkan Şenkal , Cem Emeksiz

https://doi.org/10.47897/bilmes.1406384

Abstract

This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

Keywords

Wind speed estimation, artificial neural networks, data decomposition

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