Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms

Gökhan Ekinci () and Harun Kemal Ozturk
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Gökhan Ekinci: Department of Motor Vehicles and Transportation Technologies, Vocational School of Technical Sciences, Usak University, 64200 Usak, Türkiye
Harun Kemal Ozturk: Department of Mechanical Engineering, Faculty of Engineering, Pamukkale University, 20160 Pamukkale, Türkiye

Energies, 2025, vol. 18, issue 5, 1-27

Abstract: Wind energy is a crucial renewable resource for sustainable power generation; however, challenges such as high initial investment costs and difficulties in identifying efficient locations hinder its widespread adoption. Accurate wind energy forecasting is essential for energy planning, trading, and grid optimization. This study presents short-term, medium-term, and long-term –wind power forecasts for the Söke–Çatalbük Wind Power Plant in Aydın, Turkey, using meteorological data and production records from 2018 to 2022. Five machine learning algorithms were employed—Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors Regression (KNN), and Multi-Layer Perceptron (MLP ANN)—utilizing both MinMax and Standard Scaling methods. Prediction performance was evaluated using Mean Absolute Error (MAE), Coefficient of Determination (R 2 ), and Root Mean Square Error (RMSE) metrics. The results indicate that Min-Max Scaling improved short-term predictions with KNN, while XGBoost and Random Forest provided more stable and accurate forecasts in medium- and long-term predictions. Additionally, Standard Scaling significantly enhanced MLP ANN’s performance in medium-term forecasting. These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. The results are particularly valuable for energy planners and policymakers seeking to maximize the efficiency of wind power plants and facilitate the integration of renewable energy sources into national grids more effectively.

Keywords: wind energy; machine learning; forecasting; renewable energy; multi-horizon forecasting (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: 2025
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