Journal of Green Energy Research and Innovation
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Enhancing Renewable Energy Forecasting: A Hybrid Machine Learning Approach for Solar and Wind Energy Potential in Ahvaz City

Document Type : Research article

Authors

Mehdi Mohammadian Mehr
Hossein Farzin

Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz 61357-43337, Iran.

https://doi.org/10.61882/jgeri.2.4.14
Abstract
This paper introduces a new approach for short-term forecasting of solar and wind energy potential in Ahvaz City. The method is based on the StackedBoost-XG model, a hybrid ensemble that combines Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with XGBoost as the final estimator. The study focuses on accurately predicting energy generation using real-time meteorological data. Key inputs include temperature, humidity, wind speed, and solar irradiance factors that are crucial for reliable energy forecasting. These variables are integrated into energy production formulas to estimate outputs for both solar and wind sources. This improves prediction accuracy. The model’s performance is assessed using standard evaluation metrics: RMSE, MAE, and R². Results indicate that StackedBoost-XG significantly outperforms the individual SVM and KNN models. It shows higher accuracy in forecasting both solar and wind energy. The research also explores the effect of wind turbine height. It finds that optimal energy output occurs at heights between 15 and 25 meters. In addition, the study highlights the importance of managing thermal losses in solar panels, especially during warmer months, to maintain system efficiency. Finally, it emphasizes the complementary nature of solar and wind energy. Solar power offers relatively stable output throughout the year, while wind energy provides higher peaks in specific seasons. By integrating both energy sources, the study proposes a promising solution to address energy demand imbalances in Ahvaz. This study introduces a hybrid forecasting method that uses advanced machine learning and weather data. Its goal is to optimize renewable energy systems and enhance the management of the energy grid. 

Graphical Abstract

Enhancing Renewable Energy Forecasting: A Hybrid Machine Learning Approach for Solar and Wind Energy Potential in Ahvaz City

Highlights

❖ StackedBoost-XG model combines SVM, KNN & XGBoost for better energy forecasting.
❖ Optimizes prediction accuracy, outperforming individual models in dynamic conditions.
❖ Application in Ahvaz City, tackling extreme climate & high summer energy demand.
❖ Compares SVM, KNN & StackedBoost-XG, proving superior performance in forecasting. 

Keywords

Short-term forecasting
XGBoost
SVM
KNN
StackedBoost-XG
Energy Grid Optimization
Renewable Energy Integration

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

Mehdi Mohammadian Mehr: Investigation, Methodology, Software, Roles/Writing - original draft. Hossein Farzin: Supervision, Validation, Writing-review & editing.

 

Bibliography

Mehdi Mohammadian Mehr received a Bachelor's degree in Electrical Engineering, specializing in Power Systems, from Shahid Chamran University of Ahvaz, from 2018 to 2022. He then pursued a Master's degree in Electrical Engineering with a concentration in Power Systems at the same university, from 2022 to 2024. His academic background emphasizes energy systems, particularly in the areas of renewable energy integration, smart grids, and advanced forecasting techniques. His research interests include the application of artificial intelligence in power system optimization, distribution network design and enhancement, electric load forecasting and control, power system reliability and resilience, and the integration of energy storage and renewable energy resources into power grids.

Hossein Farzin received the BSc and PhD degrees in Electrical Engineering from Sharif University of Technology, Tehran, Iran, in 2011 and 2016, respectively. He was a postdoctoral researcher at Sharif University of Technology, from 2016 to 2017. He is currently an Associate Professor in the Electrical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran. His research interests include microgrids design and optimization, integration of distributed energy resources and electric vehicles in smart grid, and power system reliability and resilience. Dr. Farzin ranked 2nd in Iran’s nationwide universities entrance exam in 2007, and is ranked among the world’s top 2% most cited researchers in 2021 and 2023. He has authored more than 60 journal and conference papers, and serves as an editor of the Scientia Iranica journal.

 

Citation

M. Mohammadian Mehr, and H. Farzin, "Enhancing Renewable Energy Forecasting: A Hybrid Machine Learning Approach for Evaluating Solar and Wind Energy Potential in Ahvaz City," Journal of Green Energy Research and Innovation, vol. 2, no. 4, pp. 14-26, 2025.

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  • Receive Date 02 May 2025
  • Revise Date 15 June 2025
  • Accept Date 01 July 2025
  • Publish Date 01 December 2025

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