Journal of Engineering, Project, and Production Management, 2025, 15(4), 2024-292
Comparative Analysis of ANN Algorithms for Wind Speed Forecasting in Renewable Energy Management
1 Assistant
Professor, Department of Alternative Energy Technology, Faculty of
Engineering and Technology, Al-Zaytoonah University of Jordan, P.O. Box
130, Amman 11733, Jordan, E-mail: y.altork@zuj.edu.jo (corresponding
author).
2 Master Student, Department of
Civil and Infrastructure Engineering, Faculty of Engineering and
Technology, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman
11733, Jordan, E-mail: duaa.qadoumi@gmail.com
3 Associate Professor, Department
of Mechanical Engineering, Faculty of Engineering and Technology, Al-Zaytoonah
University of Jordan, P.O. Box 130, Amman 11733, Jordan, E-mail:
n.shaban@zuj.edu.jo
Emgineering Management
Received December 19, 2024; revised February 1, 2025; accepted April 21, 2025
Available online October 6, 2025
Abstract: Wind energy is critical to meeting the power requirement of the global population. Accurate wind speed forecasting is vital in energy trading, power system operations, and enhanced market balance. This study examines three Artificial Neural Network (ANN) algorithms using meteorological parameters as inputs. The three methods of training that have been discussed in the current paper are the Scaled Conjugate Gradient Backpropagation technique, the Bayesian regularization algorithm, and the Levenberg-Marquardt (LM) training algorithm. In this work, 42 datasets of the total 60 datasets procured from the National Renewable Energy Laboratory (NREL) over five years were used for training, 9 for testing, and the remaining 9 for validation. Wind speed will be the study’s dependent variable while surrounding temperature, barometrical pressure, wind orientation, relative humidity, and rainfall amount are the independent variables. In the present study, the performance of the ANN algorithms is assessed using measures such as Mean Squared Error (MSE) and correlation coefficient (R). The results indicate that all three ANN algorithms exhibit excellent performance, enabling precise wind speed predictions. This reliability supports their potential application in optimizing wind energy operations. Among these, the LM algorithm provided the most precise predictions, exhibiting the lowest error rates. Therefore, it is concluded that the LM algorithm was the most accurate in predicting wind speed for the NWTC, providing valuable insights for renewable energy planning and management.
Keywords: Wind speed forecasting, Artificial Neural Network (ANN) algorithms, renewable energy management, comparative analysis, NREL datasets.
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Citation: Altork, Y., Salem, D., and Abu Shaban, N. (2025). Comparative Analysis of ANN Algorithms for Wind Speed Forecasting in Renewable Energy Management. Journal of Engineering, Project, and Production Management, 15(4), 2024-292.
DOI:
10.32738/JEPPM-2024-292
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