Short-Term Wind Energy Production Forecasting and Target Plant Selection Based on Meteorological Data Using Empirical Mode Decomposition

İsrafil Karadöl

2025

Abstract

Abstract-This study aims to identify the most suitable target wind power plant (WPP) for short-term wind energy production forecasting. Hourly meteorological data for 2022 from İzmir Province were processed using the Empirical Mode Decomposition (EMD) method to generate 56 Intrinsic Mode Function (IMF) signals, which were used as input variables for the XGBoost model. As output, production data from 52 different WPPs located within the same provincial boundaries were individually used as target variables. The model’s performance was evaluated using R², MAE, and MSE metrics. The results indicated that while high prediction accuracy was achieved for some plants, the model's performance was limited for others. The best forecast accuracy was obtained using data from WPP35, whereas the poorest performance was observed with WPP7. These findings suggest that, despite being within the same province, differences in the geographical locations of meteorological stations and WPPs, as well as region-specific meteorological characteristics, can significantly affect prediction accuracy.

Paper Citation

in Harvard Style

Karadöl İ. (2025). Short-Term Wind Energy Production Forecasting and Target Plant Selection Based on Meteorological Data Using Empirical Mode Decomposition. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 164-168. DOI: 10.5220/0014299300004848

in Bibtex Style

@conference{iceeecs25,
author={İsrafil Karadöl},
title={Short-Term Wind Energy Production Forecasting and Target Plant Selection Based on Meteorological Data Using Empirical Mode Decomposition},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={164-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014299300004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Short-Term Wind Energy Production Forecasting and Target Plant Selection Based on Meteorological Data Using Empirical Mode Decomposition
SN - 978-989-758-783-2
AU - Karadöl İ.
PY - 2025
SP - 164
EP - 168
DO - 10.5220/0014299300004848
PB - SciTePress