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

Ahmet Nur; Bayram Güre; Sabir Rüstemli; Özlem Bezek Güre

doi:10.51477/mejs.1738993

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

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.

Keywords

Ethical Statement

The authors declare that this document does not require ethics committee approval or any special permission. Our study does not cause any harm to the environment and does not involve the use of animal or human subjects.

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Details

Primary Language

English

Subjects

Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics), Power Plants

Journal Section

Research Article

Authors

Ahmet Nur ^*
0009-0006-5671-6923
Türkiye

Bayram Güre
0000-0003-1653-6451
Türkiye

Sabir Rüstemli
0000-0002-4957-1782
Türkiye

Özlem Bezek Güre
0000-0002-5272-4639
Türkiye

Publication Date

December 30, 2025

Submission Date

July 9, 2025

Acceptance Date

October 13, 2025

Published in Issue

Year 2025 Volume: 11 Number: 2

DOI

https://doi.org/10.51477/mejs.1738993

IZ

https://izlik.org/JA28KB32GU

Cite

RIS / Bibtex

APA

Nur, A., Güre, B., Rüstemli, S., & Bezek Güre, Ö. (2025). WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. Middle East Journal of Science, 11(2), 276-289. https://doi.org/10.51477/mejs.1738993

AMA

1.Nur A, Güre B, Rüstemli S, Bezek Güre Ö. WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. MEJS. 2025;11(2):276-289. doi:10.51477/mejs.1738993

Chicago

Nur, Ahmet, Bayram Güre, Sabir Rüstemli, and Özlem Bezek Güre. 2025. “WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS”. Middle East Journal of Science 11 (2): 276-89. https://doi.org/10.51477/mejs.1738993.

EndNote

Nur A, Güre B, Rüstemli S, Bezek Güre Ö (December 1, 2025) WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. Middle East Journal of Science 11 2 276–289.

IEEE

[1]A. Nur, B. Güre, S. Rüstemli, and Ö. Bezek Güre, “WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS”, MEJS, vol. 11, no. 2, pp. 276–289, Dec. 2025, doi: 10.51477/mejs.1738993.

ISNAD

Nur, Ahmet - Güre, Bayram - Rüstemli, Sabir - Bezek Güre, Özlem. “WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS”. Middle East Journal of Science 11/2 (December 1, 2025): 276-289. https://doi.org/10.51477/mejs.1738993.

JAMA

1.Nur A, Güre B, Rüstemli S, Bezek Güre Ö. WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. MEJS. 2025;11:276–289.

MLA

Nur, Ahmet, et al. “WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS”. Middle East Journal of Science, vol. 11, no. 2, Dec. 2025, pp. 276-89, doi:10.51477/mejs.1738993.

Vancouver

1.Ahmet Nur, Bayram Güre, Sabir Rüstemli, Özlem Bezek Güre. WIND SPEED PREDICTION IN MICROGRID ENERGY MANAGEMENT USING THE RANDOM FOREST AND ARTIFICIAL NEURAL NETWORKS METHODS. MEJS. 2025 Dec. 1;11(2):276-89. doi:10.51477/mejs.1738993