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Journals / Middle East Journal of Science / 2026 / Volume: 11 Issue: 2

WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS

Ahmet Nur(BITLIS EREN UNIVERSITY)0009-0006-5671-6923
Bayram Güre(BITLIS EREN UNIVERSITY)0000-0003-1653-6451
Sabir Rüstemli(BITLIS EREN UNIVERSITY)0000-0002-4957-1782
Özlem Bezek Güre(BATMAN UNIVERSITY)0000-0002-5272-4639
Pages
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Publication Date
2025-12-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the Random Forest method provides higher prediction accuracy in wind speed estimation compared to Artificial Neural Network methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Although wind energy, which is frequently utilized in microgrid systems, has relatively high initial investment costs compared to conventional energy generation systems, the low operation and maintenance costs of wind turbines make it an economically attractive alternative.
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Key Findings & Data:Therefore, accurate short-term wind speed forecasting is critically important for reliable estimation of potential production, system design, and performance-cost analysis.
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Conclusion & Impact:The results show that the Random Forest method provides higher prediction accuracy in wind speed estimation compared to Artificial Neural Network methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Microgrid · Wind Speed · Short-Term Forecasting

Abstract

Although wind energy, which is frequently utilized in microgrid systems, has relatively high initial investment costs compared to conventional energy generation systems, the low operation and maintenance costs of wind turbines make it an economically attractive alternative. However, the highly variable nature of wind speed depending on time and geography complicates the predictability of energy output, introducing uncertainties in investment decisions. Therefore, accurate short-term wind speed forecasting is critically important for reliable estimation of potential production, system design, and performance-cost analysis. In this study, the Random Forest (RF) and Artificial Neural Networks (ANN) machine learning algorithms were employed for short-term wind speed prediction. The study examines the parameters that affect the efficiency of wind systems. The dataset used in the analysis was obtained from a SCADA system associated with a wind measurement station located in Türkiye, with measurements recorded at 10-minute intervals and made available via the Kaggle platform. The dataset was partitioned into 70% for training and 30% for testing. To enhance the generalizability of the model, 10-fold cross-validation was applied. Performance evaluation of the model was conducted using metrics including MSE, RMSE, MAE, MAPE and the R². The results show that the Random Forest method provides higher prediction accuracy in wind speed estimation compared to Artificial Neural Network methods. The obtained values also confirm the suitability of this Random Forest method for short-term forecasting applications.

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Cite This Article

Nur, A., Güre, B., Rüstemli, S., Güre, Ö. B., *, A. N. (2025). WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. Middle East Journal of Science, Volume: 11 Issue: 2. https://doi.org/10.51477/mejs.1738993

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