Wind speed prediction in some major cities in Africa using Linear Regression and Random Forest algorithms

Authors

Keywords:

Energy Generation, Atmospheric Parameters, Statistical Models, Machine Learning Algorithms, African Stations

Abstract

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Dimensions

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2079

Published

2024-09-08

How to Cite

Wind speed prediction in some major cities in Africa using Linear Regression and Random Forest algorithms. (2024). Journal of the Nigerian Society of Physical Sciences, 6(4), 2079. https://doi.org/10.46481/jnsps.2024.2079

Issue

Volume 6, Issue 4, November 2024

Section

Earth Sciences
Copyright & Licensing

Copyright (c) 2024 Timothy Kayode Samson, Francis Olatunbosun Aweda

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Wind speed prediction in some major cities in Africa using Linear Regression and Random Forest algorithms. (2024). Journal of the Nigerian Society of Physical Sciences, 6(4), 2079. https://doi.org/10.46481/jnsps.2024.2079

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