Document Type : Research Article


1 Physics Programme, College of Agriculture Engineering and Science, Bowen University, P.M.B. 284, Iwo, Nigeria.

2 Mechatronics Engineering Programme, College of Agriculture Engineering and Science, Bowen University, P. M. B. 284, Iwo, Nigeria.

3 Statistics Programme, College of Agriculture Engineering and Science, Bowen University, P. M. B. 284, Iwo, Nigeria.



Wind energy has been identified as a critical component in the growth of all countries throughout the world. Nigeria has been identified as having energy issues as a result of poor maintenance of hydro and thermal energy generating stations. As a result, the current study uses some machine learning approaches over wind speed data for energy generation in the country. Machine learning models were employed for wind speed using selected meteorological parameters. Little research was done using some meteorological data and machine learning to investigate wind speed across Nigerian sub-stations, resulting in the need for further research. This research, on the other hand, focuses on a neural network for forecasting, a Long Short-Term Memory (LSTM) network model based on several fire-work algorithms (FWA). The data for this study came from the archive of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Web service, which was modeled. The LSTM predicts the wind speed model based on the FWA, which used hyper-parameter optimization and was based on a real-time prediction model that was dependent on the change and dependence of the neural network. The study data was split into two categories: test and training. According to the validation technique, the sample data was reviewed, and the first 80% of the data was utilized for training, as revealed by the (LSTM) network model. The remaining 20% of the data was used as forecast data to ensure that the model was accurate. The normalization of the data for the wind speed range of 0 to 1 which illustrates the process data, the high peak in 1985 (a = 0.12m/s, b = 0.11m/s, c = 0.13m/s d = 0.08m/s, e = 0.06m/s, f = 0.10m/s) was discovered. However, the summary result of the performances of different 11 Machine Learning algorithms of regression type for each of the seven locations in Nigeria has different values. As a result, it is recommended that this study will facilitate the prediction of wind speed for energy generation in Nigeria.


Main Subjects

  1. Staff, I.E.A., Energy balances of non-OECD countries. 2012: Organization for Economic. (
  2. Dhunny, A.Z., et al., Statistical modelling of wind speed data for Mauritius. International journal of renewable energy research, 2014. 4(4): p. 1056-1064. (
  1. Pobočíková, I. and Z. Sedliačková, Comparison of four methods for estimating the Weibull distribution parameters. Applied mathematical sciences, 2014. 8(83): p. 4137-4149. (
  1. Salahaddin, A.A., Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq. International Journal of Physical Sciences, 2013. 8(5): p. 186-192. (10.5897/IJPS12.697)
  1. Pishgar-Komleh, S., A. Keyhani, and P. Sefeedpari, Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and sustainable energy reviews, 2015. 42: p. 313-322. (
  2. Agbo, E.P., et al., Solar energy: A panacea for the electricity generation crisis in Nigeria. Heliyon, 2021. 7(5): p. e07016. (
  3. Aliyu, A.S., J.O. Dada, and I.K. Adam, Current status and future prospects of renewable energy in Nigeria. Renewable and sustainable energy reviews, 2015. 48: p. 336-346. (
  1. Oyedepo, S.O., Towards achieving energy for sustainable development in Nigeria. Renewable and sustainable energy reviews, 2014. 34: p. 255-272. (
  1. Asiegbu, A., Studies of wind resources in Umudike, south east Nigeria-an assessment of economic viability. 2007.


  1. Munteanu, I., et al., Optimal control of wind energy systems: towards a global approach. Vol. 22. 2008: Springer. (10.1109/MCS.2009.932326)
  2. Ohunakin, O.S. and O.O. Akinnawonu, Assessment of wind energy potential and the economics of wind power generation in Jos, Plateau State, Nigeria. Energy for sustainable Development, 2012. 16(1): p. 78-83.(
  3. Justus, C., et al., Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology (1962-1982), 1978: p. 350-353. (<0350:MFEWSF>2.0.CO;2)
  1. Martin, M., L. Cremades, and J. Santabarbara, Analysis and modelling of time series of surface wind speed and direction. International Journal of Climatology: A Journal of the Royal Meteorological Society, 1999. 19(2): p. 197-209. (<197::AID-JOC360>3.0.CO;2-H)
  1. Chang, T.P., Estimation of wind energy potential using different probability density functions. Applied Energy, 2011. 88(5): p. 1848-1856.(
  2. Chang, T.P., Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 2011. 88(1): p. 272-282. (
  1. Waewsak, J., et al., An analysis of wind speed distribution at Thasala, Nakhon Si Thammarat, Thailand. Journal of sustainable energy & environment, 2011. 2(2): p. 51-55. (
  1. Kaoga, D.K., et al., Comparison of five numerical methods for estimating Weibull parameters for wind energy applications in the district of Kousseri, Cameroon. Asian Journal of Natural & Applied Sciences, 2014. 3(1): p. 72.


  1. Mert, I. and C. Karakuş, A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey. Turkish Journal of Electrical Engineering and Computer Sciences, 2015. 23(6): p. 1571-1586. (10.3906/elk-1402-66)
  2. Antor, A.F. and E.D. Wollega. Comparison of machine learning algorithms for wind speed prediction. in Proceedings of the International Conference on Industrial Engineering and Operations Management. 2020.( (
  3. Oyewole, J. A., F. O. Aweda, and D. Oni, Comparison Of Three Numerical Methods For Estimating Weibull Parameters Using Weibull Distribution Model In Nigeria. Nigerian Journal of Basic and Applied Sciences, 2019. 27(2): p. 8-15.(10.4314/njbas.v27i2.2)
  4. Ma, Z., et al., Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction. Energy Conversion and Management, 2020. 205: p. 112345.(
  5. Memarzadeh, G. and F. Keynia, A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Conversion and Management, 2020. 213: p. 112824.(
  6. Moreno, S.R., et al., Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Conversion and Management, 2020. 213: p. 112869.(
  7. Wang, C., H. Zhang, and P. Ma, Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network. Applied Energy, 2020. 259: p. 114139. (
  1. Uçar, M.K., et al., The effect of training and testing process on machine learning in biomedical datasets. Mathematical Problems in Engineering, 2020. 2020. (
  1. Khosravi, A., et al., Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustainable Energy Technologies and Assessments, 2018. 25: p. 146-160. (
  1. Olubi, O., E. Oniya, and T. Owolabi, Development of predictive model for radon-222 estimation in the atmosphere using stepwise regression and grid search based-random forest regression. Journal of the Nigerian Society of Physical Sciences, 2021: p. 132-139. (
  1. Rodríguez, F., et al., Very short-term wind power density forecasting through artificial neural networks for microgrid control. Renewable energy, 2020. 145: p. 1517-1527. (
  1. Zafirakis, D., G. Tzanes, and J.K. Kaldellis, Forecasting of wind power generation with the use of artificial neural networks and support vector regression models. Energy Procedia, 2019. 159: p. 509-514.(
  2. Brahimi, T., et al., Prediction of wind speed distribution using artificial neural network: The case of Saudi Arabia. Procedia Computer Science, 2019. 163: p. 41-48. (
  1. Navas, R.K.B., S. Prakash, and T. Sasipraba, Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India. Physica A: Statistical Mechanics and its Applications, 2020. 542: p. 123383. (
  1. Lawan, S., W. Abidin, and T. Masri, Implementation of a topographic artificial neural network wind speed prediction model for assessing onshore wind power potential in Sibu, Sarawak. The Egyptian Journal of Remote Sensing and Space Science, 2020. 23(1): p. 21-34. (
  1. Lin, Z., X. Liu, and M. Collu, Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems, 2020. 118: p. 105835.(
  2. Nielson, J., et al., Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction. Energy, 2020. 190: p. 116273. (
  1. Hassani, Z., et al., A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization. Journal of Sciences, Islamic Republic of Iran, 2020. 31(2): p. 165-173. (10.22059/JSCIENCES.2020.288729.1007444)
  2. Venkatakrishnan, G., et al., Implementation of Modified Differential Evolution Algorithm for Hybrid Renewable Energy System. Journal of the Nigerian Society of Physical Sciences, 2021: p. 209-215.(
  3. Zhang, Z., et al., Long short-term memory network based on neighborhood gates for processing complex causality in wind speed prediction. Energy Conversion and Management, 2019. 192: p. 37-51.(
  4. Liu, X., et al., Wind speed forecasting using deep neural network with feature selection. Neurocomputing, 2020. 397: p. 393-403.
  5. Hosseini, E., et al., Control of Pitch Angle in Wind Turbine Based on Doubly Fed Induction Generator Using Fuzzy Logic Method. Journal of Renewable Energy and Environment, 2022. 9(2): p. 1-7.( 10.30501/JREE.2021.293546.1226)
  6. Maheri, A., I.K. Wiratama, and T. Macquart, Performance of Microtabs and Trailing Edge Flaps in Wind Turbine Power Regulation: A Numerical Analysis Using WTSim. Journal of Renewable Energy and Environment, 2022. 9(2): p. 18-26. (10.30501/JREE.2021.291397.1220)
  7. Shao, B., et al., Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm. Advances in Materials Science and Engineering, 2021. 2021.(
  8. Adebayo, S., et al., Refractive Index Perception and Prediction of Radio wave through Recursive Neural Networks using Meteorological Data Parameters. International Journal of Engineering, 2013. 35(4): p. 810-818.(10.5829/IJE.2022.35.04A.21)
  9. Yin, W., et al., Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923, 2017.
  10. Aweda, F.O., S. J. Olufemi, and J. O. Agbolade, Meteorological Parameters Study and Temperature Forecasting in Selected Stations in Sub-Sahara Africa using MERRA-2 Data. Nigerian Journal of Technological Development, 2022. 19(1): p. 80-91. (10.4314/njtd.v19i1.9)
  11. Aweda, F. O., et al., Variation of the Earth’s Irradiance over Some Selected Towns in Nigeria. Iranian (Iranica) Journal of Energy & Environment, 2020. 11(4): p. 301-307. (10.5829/IJEE.2020.11.04.08)
  1. Aweda, F. O., et al., Modelling Net Radiative Measurement of Meteorological Parameters Using MERRA-2 Data in Sub-Sahara African Town. Iranian (Iranica) Journal of Energy & Environment, 2021. 12(2): p. 173-180.( 10.5829/IJEE.2021.12.02.10)
  2. Gelaro, R., et al., The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of climate, 2017. 30(14): p. 5419-5454.(
  3. Rojas, R., AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Freie University, Berlin, Tech. Rep, 2009.(
  4. Junior, J.R.B. and M. do Carmo Nicoletti, An iterative boosting-based ensemble for streaming data classification. Information Fusion, 2019. 45: p. 66-78.(
  5. Ma, X., et al., Prediction of extreme wind speed for offshore wind farms considering parametrization of surface roughness. Energies, 2021. 14(4): p. 1033.(10.3390/en14041033)
  6. Samadianfard, S., et al., Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports, 2020. 6: p. 1147-1159.(
  7. Nezhad, M.M., et al., Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands. Renewable Energy, 2020. 155: p. 212-224. (
  8. Nezhad, M.M., et al., Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies. Renewable and Sustainable Energy Reviews, 2022. 168: p. 112791.                 (
  1. Neshat, M., et al., Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies. Energy Conversion and Management, 2022. 259: p. 115590. (
  1. Nezhad, M.M., et al., A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models. Renewable Energy, 2022. 190: p. 156-166. (
  1. Aweda, F.O., et al., Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations. Journal of the Nigerian Society of Physical Sciences, 2022: p. 820-820.        (
  1. Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence(pp. 355-364). Springer, Berlin, Heidelberg. (
  1. Tan, Y., Yu, C., Zheng, S., & Ding, K. (2013). Introduction to fireworks algorithm. International Journal of Swarm Intelligence Research (IJSIR)4(4), 39-70. (
  1. Zheng, S., Janecek, A., & Tan, Y. (2013, June). Enhanced fireworks algorithm. In 2013 IEEE congress on evolutionary computation(pp. 2069-2077). IEEE. (
  1. Li, J., & Tan, Y. (2019). A comprehensive review of the fireworks algorithm. ACM Computing Surveys (CSUR)52(6), 1-28. (
  2. Zhang, B., Zheng, Y. J., Zhang, M. X., & Chen, S. Y. (2015). Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM transactions on computational biology and bioinformatics14(1), 42-55. (1109/TCBB.2015.2446487).
  3. Pei, Y., Zheng, S., Tan, Y., & Takagi, H. (2012, October). An empirical study on influence of approximation approaches on enhancing fireworks algorithm. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(pp. 1322-1327). IEEE. (1109/ICSMC.2012.6377916)
  4. Shao, B., Song, D., Bian, G., & Zhao, Y. (2021). Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm. Advances in Materials Science and Engineering2021. (
  5. Akhila, P., Anjana, R. L. S., & Kavitha, M. (2022, March). Climate Forecasting: Long short Term Memory Model using Global Temperature Data. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC)(pp. 469-473). IEEE. (1109/ICCMC53470.2022.9753779).
  6. Moghadasi, M., H.A. Ozgoli, and F. Farhani, Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches. International Journal of Energy Research, 2021. 45(1): p. 879-893. (