An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports
Wind speed patterns are highly dynamic and non-linear and thus cannot be accurately forecasted using conventional linear regression models. In this work, Artificial Neural Network (ANN) technique was applied to forecast wind speeds and directions in airports. Monthly data of maximum temperature, minimum temperature, wind speed, wind direction, relative humidity and wind run for Yola International Airport were collected from 1995 to 2021 from Nigerian Meteorological Agency (NIMET) Abuja-Nigeria. Six Neural Network models were built. ANN with no hidden layers, ANN model with one hidden layer and two dropout layers, ANN model with four hidden layers and three dropout layers, ANN model with eight hidden layers, ANN model with nine hidden layers and finally, ANN model with ten hidden layers. Back Propagation training algorithm was implemented using the PYTHON toolbox. Each of the models was trained using the training dataset and validated using the validation dataset. To test the forecasting ability of each of the models we tested it using unknown data that is the test dataset. The results from each of the models were organized and assessed in terms of the magnitude of the statistical error between the measured result and the real data. This was achieved by measuring the average of the Mean Square Errors (MSE) and Mean Absolute Error (MAE) for each of the models used for forecasting both wind speeds and directions. The results show that Multilayer perceptron with ten hidden layers with (MSE) = 0.92 and (MAE) = 0.73 emerged as the most preferred model for wind speeds forecast while the multilayer perceptron with four hidden layers with (MSE) = 1,858 and (MAE) = 35 emerged the most preferred model for wind directions forecast. Future research can be carried out to improve the accuracy of the model for wind direction forecasts.
Ahrens CD, Henson R. Essentials of meteorology: An invitation to the atmosphere. Cengage Learning; 2016 Dec 5.
DeFreitas NC, Silva MP, Sakamoto MS. Wind speeds forecasting: a review. Int. J. Eng. Res. Appl., 2018; 8:4-9.
Larraondo PR, Inza I, Lozano JA. A system for airport weather forecasting based on circular regression trees. Environmental modelling & software, 2018; Feb 1; 100:24-32.
Rotich N. Forecasting of wind speeds and directions with artificial neural networks. 2014. Oct.
Rozas-Larraondo P, Inza I, Lozano JA. A method for wind speed forecasting in airports based on nonparametric regression. Weather and Forecasting, 2014; 29(6):1332-42.
Clark DA, Ferris RF, Moradi DD. Airport Wind Observations Architectural Analysis. Massachusetts INST of Tech Lexington; 2018 Jul 10.
Jung J, Broadwater RP. Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 2014 Mar 1; 31:762-77.
Früh WG. Evaluation of simple wind power forecasting methods applied to a long-term wind record from Scotland. In International Conference on Renewable Energies and Power Quality (ICREPQ’12), Santiago de Compostela 2012 Mar.
Monfared M, Rastegar H, Kojabadi HM. A new strategy for wind speed forecasting using artificial intelligent methods. Renewable energy, 2009; Mar 1; 34(3):845-8.
Demuth H, Beale M. Neural Network Toolbox User’S Guide: For Use with Matlab, Version 4.
Esen H, Inalli M, Sengur A, Esen M. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy and Buildings, 2008; Jan 1; 40(6):1074-83.
Shiruru, K. An Introduction to Artificial Neural Network. An international journal of Advance Research and Innovative Ideas in Education, 2016, 27.
Du, K. L., & Swamy, M. N. Neural networks and statistical learning. Canada: Springer Science & Business Media, 2013 July.
Wesonga R, Nabugoomu F, Ababneh F, Owino A. Simulation of time series wind speed at an international airport. Simulation, 2019; 95(2):171-84.
Kaur T, Kumar S, Segal R. Application of artificial neural network for short term wind speed forecasting. In 2016 Biennial international conference on power and energy systems: towards sustainable energy (PESTSE) 2016 Jan 21 (pp. 1-5). IEEE.
Fazelpour F, Tarashkar N, Rosen MA. Short-term wind speed forecasting using artificial neural networks for Tehran, Iran. International Journal of Energy and Environmental Engineering, 2016; 7(4):377-90.
Hu Q, Zhang S, Yu M, Xie Z. Short-term wind speed or power forecasting with heteroscedastic support vector regression. IEEE Transactions on Sustainable Energy, 2015 Nov 23; 7(1):241-9.
Hussin NH, Yusof F, Norrulashikin SM. Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models. Pertanika Journal of Science & Technology, 2021 Jan 1; 29(1).
Liu H, Tian HQ, Chen C, Li YF. An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization. International Journal of Electrical Power & Energy Systems, 2013; Nov 1; 52:161-73.
Shi, J., Guo, J., & Zheng, S. Evaluation of hybrid forecasting approach for wind speed and power generation time series. Journal of Renewable and Sustainable Energy Review, 2012;16:80-3471.
Jung S, Kwon SD. Weighted error functions in artificial neural networks for improved wind energy potential estimation. Applied Energy, 2013 Nov 1; 111:778-90.
This work is licensed under a Creative Commons Attribution 4.0 International License.