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

S. Emeis, ”Current issues in wind energy meteorology”, Meteorological Application 21 (2014) 803. https://doi.org/10.1002/met.1472.

A. Routray, K. D. Mistry, S. R. Arya & B. Chittibabu, ”Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 43 (2021) 1. https://doi.org/10.1080/15567036.2020.1859010.

H. Acikgoz, C. Yildiz & M. Sekkeli, ”An extreme learning machine based very short-term wind power forecasting method for complex terrain”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42 (2020) 2715. https://doi.org/10.1080/15567036.2020.1755390.

Z. Tian, Y. Ren & G. Wang, ”Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 41 (2019) 26. https://doi.org/10.1080/15567036.2018.1495782.

V. K. Saini, R. Kumar, A. Mathur & A. Saxena, ”Short term forecasting based on hourly wind speed data using deep learning algorithms”, 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India, 2020, pp. 1-6. https://doi.org/10.1109/ICETCE48199.2020.9091757.

M. Ibrahim, A. Alsheikh, Q. Al-Hindawi, S. Al-Dahidi & H. ElMoaqet, ”Short-time wind speed forecast using artificial learning-based algorithms”, Computational intelligence and neuroscience 2020 (2020) 1. https://doi.org/10.1155/2020/8439719.

L. Liu, & Y. Liang, Wind power forecast optimization by integration of CFD and Kalman filtering. Part A: Recovery, Utilization, and Environmental Effects, Energy Sources 43 (2021) 1880. https://doi.org/10.1080/15567036.2019.1668080.

P. Gross, A. Boulanger, M. Arias, D. L. Waltz, P. M. Long, C. Lawson, R. N. Anderson, M, Koenig, M. Mastrocinque, W. Fairechio, J. A. Johnson, S. Lee, F. Doherty & A. Kressner, ”Predicting electricity distribution feeder failures using machine learning susceptibility analysis”, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, Boston, Massachusetts, USA, 2006, pp. 1705-1711. https://api.semanticscholar.org/CorpusID:6175166.

K. Mahmoud, M. Abdelnasser, H. Kashef, D. Puig & M. Lehtonen, ”Machine learning based method for estimating energy losses in large-scale unbalanced distribution systems with photovoltaics”, Artificial Intelligence and Sensor Informatics: Exploring Smart City and Construction Business Implications 6 (2020) 157. https://doi.org/10.9781/ijimai.2020.08.002.

F. O. Aweda, S. Adebayo, A. A. Adeniji, T. K. Samson & J. A. Akinpelu, ”Investigation of wind speed to generate energy using machine learning algorithms approach over selected Nigerian stations”, Journal of Renewable Energy and Environment 10 (2023) 81. https://doi.org/10.30501/jree.2022.354698.1422.

J. A. Oyewole, F. O. Aweda & D. Oni, “Comparison of three numerical methods for estimating weibull parameters using weibull distribution model in Nigeria”, Nigerian Journal of Basic and Applied Sciences 27 (2019) 8. https://doi.org/10.4314/njbas.v27i2.2.

F. O. Aweda & T. K. Samson, ”Modelling the Earth’s Solar Irradiance Across Some Selected Stations in Sub-Sahara Region of Africa”, Iranian (Iranica) Journal of Energy & Environment 11 2020 204. https://doi.org/10.5829/ijee.2020.11.03.05.

M. Okada, T. Ichizawa, Y. Nakamura, K. Yamaguchi, R. Kodama, H. Kato, Y. Nagano, R. Ikeda, V. Q. Doan, H. Kusaka & N Ogasawara ”Development of a wind power ramp forecast system by astatistical and meteorological approach”, in Grand Renewable Energy proceedings Japan council for Renewable Energy, Yokohama, Japan, 2018, pp. 130–133. https://web.archive.org/web/20220506190938id/https://www.jstage.jst.go.jp/article/gre/1/0/1130/pdf

Y. Nie, H. Bo, W. Zhang & H. Zhang, ”Research on hybrid wind speed prediction system based on artificial intelligence and double prediction scheme”, Complexity 2020 (2020) 1. https://doi.org/10.1155/2020/9601763.

J. M. Alvarez-Alvarado, J. G. R´ ´?os-Moreno, E. J. Ventura-Ramos, G. Ronquillo-Lomeli & M. Trejo-Perea, ”An alternative methodology to evaluate sites using climatology criteria for hosting wind, solar, and hybrid plants”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 41 (2020) 1. https://doi.org/10.1080/15567036.2020.1772911.

S. Louassa, O. Guerri, A. Kaabeche & N. Yassaa, ”Effects of local ambient air temperatures on wind park performance: case of the Kaberten wind park”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 45 (2023) 6082. https://doi.org/10.1080/15567036.2019.1673509.

G. Venkatakrishnan, R. Rengaraj, K. Sathish, R. Dinesh & T. Nishanth, ”Implementation of Modified Differential Evolution Algorithm for Hybrid Renewable Energy System”, Journal of the Nigerian Society of Physical Sciences 3 (2021) 209. https://doi.org/10.46481/jnsps.2021.240.

Y. Zhang, G. Pan, B. Chen, J. Han, Y. Zhao & C. Zhang, ”Short-term wind speed prediction model based on GA-ANN improved by VMD”, Renewable Energy 156 (2020) 1373. https://doi.org/10.1016/j.renene.2019.12.047.

Y. Zhang, S. Gao, J. Han & M. Ban, ”Wind speed prediction research considering wind speed ramp and residual distribution”, IEEE Access 79 2019 131873. https://doi.org/10.1109/ACCESS.2019.2940897.

Y. Jiang, G. Huang, Q. Yang, Z. Yan & C. Zhang, ”A novel probabilistic wind speed prediction approach using real time refined variational model decomposition and conditional kernel density estimation”, Energy Conversion and Management 185 (2019) 758. https://doi.org/10.1016/j.enconman.2019.02.028.

J. Wang & Y. Li, ”An innovative hybrid approach for multi-step ahead wind speed prediction”, Applied Soft Computing 78 (2019) 296. https://doi.org/10.1016/j.asoc.2019.02.034.

S. O. Adams, D. A. Obaromi & A. A. Irinews, ”Goodness of Fit Test of an Autocorrelated Time Series Cubic Smoothing Spline Model”, Journal of the Nigerian Society of Physical Sciences 3 (2021) 191. https://doi.org/10.46481/jnsps.2021.265.

Y. Chen, Z. Dong, Y. Wang, J. Su, Z. Han, D. Zhou, K. Zhang, Y Zhao & Y. Bao, ”Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history”, Energy Conversion and Management 227 (2021) 113559. https://doi.org/10.1016/j.enconman.2020.113559.

R. Anjana, ”Fuzzy and PI Based Speed Control of BLDC Motor using Bidirectional Converter for Electric Vehicle Application”, Trends in Electrical Engineering 8 (2019) 35. https://doi.org/10.1109/GET.2016.7916647.

F. O. Aweda, & T. K. Samson, ”Comparative analysis of trigonometric and polynomial models in meteorological parameter prediction for Sub-Saharan West African stations”, Acadlore Trans. Geosci. 3 (2014) 1. https://doi.org/10.56578/atg030101.

M. Jamil & M. Zeeshan, ”A comparative analysis of ANN and chaotic approach-based wind speed prediction in India”, Neural Computing and Applications 31 (2019) 6807. https://doi.org/10.1007/s00521-018-3513-2.

J. Wang, N. Zhang, & H. Lu, ”A novel system based on neural networks with linear combination framework for wind speed forecasting”, Energy conversion and management, 181 (2019) 425. https://doi.org/10.1016/j.enconman.2018.12.020.

H. Malik & A. K. Yadav, ”A novel hybrid approach based on relief algorithm and fuzzy reinforcement learning approach for predicting wind speed”, Sustain Energy Technol Assess 43 (2021) 1. https://doi.org/10.1016/j.seta.2020.100920.

D. Demetriou, C. Michailides, G. Papanastasiou & T. Onoufriou, ”Coastal zone significant wave height prediction by supervised machine learning classification algorithms”, Ocean Engineering 221 (2021) 108592. https://doi.org/10.1016/j.oceaneng.2021.108592.

M. Murat, I. Malinowska, M. Gos & J. Krzyszczak, ”Forecasting daily meteorological time series using ARIMA and regression models”, International agrophysics 32 (2018) 253. https://doi.org/10.1515/intag-2017-0007.

M. Murat, I. Malinowska, H. Hoffmann & P. Baranowski, ”Statistical modelling of agrometeorological time series by exponential smoothing”, International Agrophysics 30 (2016) 57. https://doi.org/10.1515/intag-2015-0076.

A. Chaudhary, A. Sharma, A. Kumar, K. Dikshit & N. Kumar, ”Short term wind power forecasting using machine learning techniques”, Journal of Statistics and Management Systems 23 2020 145. https://doi.org/10.1080/09720510.2020.1721632.

S. Makridakis, E. Spiliotis & V. Assimakopoulos, ”Statistical and Machine Learning forecasting methods: Concerns and ways forward”, PloS one 13 (2018) e0194889. https://doi.org/10.1371/journal.pone.0194889.

S. A. Taher & M. H. Karimi, ”Optimal reconfiguration and DG allocation in balanced and unbalanced distribution systems”, Ain Shams Engineering Journal 5 (2014) 735. https://doi.org/10.1016/j.asej.2014.03.009.

M. S. K. Reddy & K. Selvajyothi, ”Optimal placement of electric vehicle charging station for unbalanced radial distribution systems”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 41 (2020) 1. https://doi.org/10.1080/15567036.2020.1731017.

C. A. Patel, K. Mistry & R. Roy, ”Loss allocation in radial distribution system with multiple DG placement using TLBO”, IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2015, pp. 1-5. https://doi.org/10.1109/ICECCT.2015.7225932.

R. T. Bhimarasetti & A. Kumar, ”Distributed generation placement in unbalanced distribution system with seasonal load variation”, 2014 Eighteenth National Power Systems Conference (NPSC), Guwahati, India, 2014, pp. 1-5. https://doi.org/10.1109/NPSC.2014.7103786.

R. V. Rao, V. J. Savsani & D. Vakharia, ”Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-aided design 43 (2011) 303. https://doi.org/10.1016/j.cad.2010.12.015.

S. Mishra, C. Bordin, K. Taharaguchi & I. Palu, ”Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature”, Energy Reports. 6 (2020) 273. https://doi.org/10.1016/j.egyr.2019.11.009.

F. O. Aweda & T. K. Samson, ”Relationship between Air, Temperature and Rainfall variability of selected stations in Sub-Sahara Africa”, Iranian (Iranica) Journal of Energy & Environment 13 (2022) 248. https://www.ijee.net/article1497384cc173522dfb8db1e1fe78bae3e13aae.pdf.

C. Y. Ho, K. S. Cheng & C. H. Ang, “Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan”, Energies 16 (2023) 1374. https://doi.org/10.3390/en16031374.

L. B. dos Santos, L. S. Monteiro, R. Santos, M. A. Felipe Santos, M. C. C. de O. Carvalho & M. B. Figueredo, ”Wind speed prediction model based on DWT and Randon Forest, Concilium 23 (2023) 347. https://doi.org/10.53660/CLM-2447-23S44.

L. Zheng, Z. Shaohui, Y. Yingxin, S. Yi & G. Zhiqiu, ”Short-Term Wind Power Prediction Model Based on WRF-RF Model”, 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China, 2023, pp. 599-604. http://dx.doi.org/10.1109/ICCCBDA56900.2023.10154834.

C. A. Fitipaldi, R. P. Monteiro & D. Pinheiro ”Forecasting of Wind Power Generation Cristalandia wind farm using Tree-Based Machine Learn-ˆ ing Approaches”, XVI Brazilian Conference on Computational Intelligence (CBIC 2023), Salvador, 2023, pp. 1-6. http://dx.doi.org/10.21528/CBIC2023-028.

S. Karasu, A. Altan, A., Z. Sarac¸ & F. Hacioglu, ”Estimation of Wind Speed by using Regression Learners with Different Filtering Methods”, 1st International Conference on Energy Systems Engineering, KBU—Karabuk, Turkey, 2017, pp. 7–11. https://www.researchgate.net/publication/322642431_Estimation_of_Wind_Speed_by_using_Regression_Learners_with_Different_Filtering_Methods.

R. Mamani & P. Hendrick, ”Weather research and forecasting model and MERRA-2 data for wind energy evaluation at different altitudes in Bolivia”, Wind Engineering 46 (2022) 177. https://doi.org/10.1177/0309524X211019701.

J. M. Bright, X. Bai, Y. Zhang, X. Sun, B. Acord & P. Wang, ”irradpy: Python package for MERRA-2 download, extraction and usage for clearsky irradiance modelling”, Solar Energy 199 (2020) 685. https://doi.org/10.1016/j.solener.2020.02.061.

R. Gelaro, W. McCarty, M. J. Su'{a}rez, R. Todling, A. Molod, L. Takacs, C. Randles, A. Darmenov, M. Bosilovich , R. Reichle, K. Wargan, L. Coy, R. Cullather, C. Draper, S. Akella, V. Buchard, A. Conaty, A. da Silva, W. Gu, G. K. Kim, R. Koster, R. Lucchesi, D. Merkova, J. E. Nielsen, G. Partyka, S. Pawson, W. Putman, M. Rienecker, S. D. Schubert, M. Sienkiewicz, B. Zhao, ”The modern-era retrospective analysis for research and applications, version 2 (MERRA-2)”, Journal of climate 30 (2017) 5419. https://doi.org/10.1175/JCLI-D-16-0758.1.

F. O. Aweda, J. A. Akinpelu, T. K. Samson, M. Sanni & B. S. Olatinwo, ”Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations”, Journal of the Nigerian Society of Physical Sciences 4 (2022) 820. https://doi.org/10.46481/jnsps.2022.820.

G. V. Drisya, P. Valsaraj, K. Asokanb & K. S. Kumar, “Wind speed forecast using random forest learning method”, International Journal on Computer Science and Engineering (IJCSE) 9 (2017) 362. https://doi.org/10.48550/arXiv.2203.14909.

M. Y. Khan, A. Qayoom, M. S. Nizami, M. S. Siddiqui, S. Wasi & S. M. K. Raazi, ”Automated prediction of Good Dictionary EXamples (GDEX): A comprehensive experiment with distant supervision, machine learning, and word embedding-based deep learning techniques”, Hindawi Complexity 2021 (2021) 1. https://doi.org/10.1155/2021%2F2553199.

R. Meenal, P. A. Michael, D. Pamela & E. Rajasekaran, “Weather prediction using random forest machine learning model”, Indonesian Journal of Electrical Engineering and Computer Science 22 (2021) 1208. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1208-1215.

G. G. Samuel, G. F. Salimath, T. Porselvi & V. Karthikeyan, “Improved Prediction of Wind Speed Using Machine Learning.Journal of Physics”, Conference Series 1964 (2021) 052005. https://doi.org/10.1088/1742-6596/1964/5/052005.

A. Verma, K. G. Upadhyay & M. M. Tripathi “Development of Artificial Intelligent techniques for Short-Term wind speed forecasting”, International Journal of Engineering Trends and Technology 69 (2021) 56. https://doi.org/10.14445/22315381/IJETT-V69I7P208.

T. K. Samson & F. O. Aweda, ”Seasonal autoregressive integrated moving average modelling and forecasting of monthly rainfall in selected African stations”, Mathematical Modelling of Engineering Problems 11 (2024) 159. http://dx.doi.org/10.18280/mmep.110117.

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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