Calculating the yearly energy output, which keeps the balance between both the generation and consumption of electricity, is made easier with the use of wind energy production estimates for grid interfaces. Effective wind speed forecasting is crucial for achieving this goal. In this research, linear statistical models of prediction Generalized Autoregressive Score (GAS), GAS model with exogenous variable x (GASX), and Autoregressive Integrated Moving Average (ARIMA) are the models used to estimate wind speed accurately. Additionally, the modeling of non-linear time-series data has been done using the Non- Linear GASX (NLGASX) statistical predictive modeling method. Additionally, the REctified Linear Unit (RELU), Softmax, Hyperbolic Tangent (TANH), and Sigmoid modeling approaches are used to optimize the suggested NLGASX model. In comparison to existing models, the suggested optimized NLGASX approach performs significantly better. In order to anticipate wind power, the wind power curve modeling additionally takes wind speed as an input. The estimated wind power has been used to determine the yearly energy.

[1] Ai B., Yang H., Shen H., and Liao X., “Computer- Aided Design of PV/Wind Hybrid System,” Renewable Energy, vol. 28, no. 10, pp. 1491-1512, 2003. https://doi.org/10.1016/S0960-1481(03)00011-9

[2] Ardia D., Boudt K., and Catania L., “Generalized Autoregressive Score Models in R: The GAS Package,” Journal of Statistical Software, vol. 88, no. 6, pp. 1-28, 2019. https://doi.org/10.18637/jss.v088.i06

[3] Creal D., Koopman S., and Lucas A., “Generalized Autoregressive Score Models with Applications,” Journal of Applied Econometrics, vol. 28, pp. 777-795, 2013. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1 002/jae.1279

[4] Goodfellow I., Bengio Y., and Courville A., Deep Learning, The MIT Press, 2016. https://www.deeplearningbook.org/

[5] Guo Z., Zhao W., Lu H., and Wang J., “Multi-Step Forecasting for Wind Speed Using a Modified EMD-Based Artificial Neural Network Model,” Renewable Energy, vol. 37, no. 1, pp. 241-249, 2012. https://doi.org/10.1016/j.renene.2011.06.023

[6] Guo Z., Zhao J., Zhang W., and Wang J., “A Corrected Hybrid Approach for Wind Speed Prediction in Hexi Corridor of China,” Energy, vol. 36, no. 3, pp. 1668-1679, 2011. https://doi.org/10.1016/j.energy.2010.12.063

[7] Han J. and Moraga C., “The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning,” in Proceedings of the International Workshop on Artificial Neural Networks, Malaga, pp. 195-201, 1995. https://doi.org/10.1007/3-540-59497-3_175

[8] Harvey A., Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series, Cambridge University Press, 2013. https://doi.org/10.1017/CBO9781139540933

[9] Jangamshetti S. and Rau V., “Site Matching of Wind Turbine Generators: A Case Study,” IEEE Energy Conversion, vol. 14, no. 4, pp. 1537-1543, 1999. DOI:10.1109/60.815102

[10] Jursa R. and Rohrig K., “Short-Term Wind Power Forecasting Using Evolutionary Algorithms for the Automated Specification of Artificial Intelligence Models,” International Journal of Forecasting, vol. 24, no. 4, pp. 694-709, 2008. https://doi.org/10.1016/j.ijforecast.2008.08.007

[11] Kazemi M. and Goudarzi A., “A Novel Method for Estimating Wind Turbines Power Output Based on Least Square Approximation,” Usage of Statistical Techniques to Monitor the Performance of Wind Turbines 349 International Journal of Engineering and Advanced Technology, vol. 2, no. 1, pp. 97-101, 2012. file:///C:/Users/user/Downloads/A0704092112.pdf

[12] Krizhevsky A., Sutskever I., and Hinton G., “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017. https://doi.org/10.1145/3065386

[13] Lei M., Shiyan L., Chuanwen J., Hongling L., and Yan Z., “A Review on the Forecasting of Wind Speed and Generated Power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915-920, 2009. https://doi.org/10.1016/j.rser.2008.02.002

[14] Maatallah O., Achuthan A., Janoyan K., and Marzocca P., “Recursive Wind Speed Forecasting Based on Hammerstein Auto-Regressive Model,” Applied Energy, vol. 145, pp. 191-197, 2015. https://doi.org/10.1016/j.apenergy.2015.02.032

[15] Mao M., Ling J., Chang L., Hatziargyriou N., Zhang J., and Ding Y., “A Novel Short-Term Wind Speed Prediction Based on MFEC,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 4, no. 4, pp. 1206-1216, 2016. DOI:10.1109/JESTPE.2016.2590834

[16] Nair V. and Hinton G., “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on Machine Learning, Haifa, pp. 1-8, 2010. https://www.cs.toronto.edu/~fritz/absps/reluICM L.pdf

[17] National Renewable Energy Laboratory (NREL) 2007-2008 Western dataset. Site-id 72509, http://wind.nrel.gov/Web_nrel/2007, Last Visited, 2024. https://www.nrel.gov/grid/western-wind- data.html

[18] Pallabazzer R., “Evaluation of Wind-Generator Potentiality,” Solar Energy, vol. 55, no. 1, pp. 49- 59, 1995. https://doi.org/10.1016/0038- 092X(95)00040-X

[19] Pinto T., Ramos S., Sousa T., and Vale Z., “Short- Term Wind Speed Forecasting Using Support Vector Machines,” in Proceedings of the IEEE Computational Intelligence Dynamic Uncertain Environments Symposium, Orlando, pp. 40-46, 2014. DOI: 10.1109/CIDUE.2014.7007865

[20] Santamara-Bonfil G., Reyes-Ballesteros A., and Gershenson C., “Wind Speed Forecasting for Wind Farms: A Method Based on Support Vector Regression,” Renewable Energy, vol. 85, pp. 790- 809, 2016. https://doi.org/10.1016/j.renene.2015.07.004

[21] Shohoni V., Gupta S., and Nema R., “A Comparative Analysis of Wind Speed Probability Distributions for Wind Power Assessment of four Sites,” Turkish Journal of Electrical Engineering and Computer Science, vol. 24, no. 6, pp. 4724- 4735, 2016. https://journals.tubitak.gov.tr/elektrik/vol24/iss6/14/

[22] Thapar V., Agnihotri G., and Sethi V., “Critical Analysis of Methods for Mathematical Modelling of Wind Turbines,” Renewable Energy, vol. 36, no. 11, pp. 3166-3177, 2011. https://doi.org/10.1016/j.renene.2011.03.016

[23] Uqaili I. and Ahsan S., “Machine Learning Based Prediction of Complex Bugs in Source Code,” The International Arab Journal of Information Technology, vol. 17, no. 1, pp. 26-37, 2020. DOI:10.34028/iajit/17/1/4

[24] Zhang W., Wang J., Wang J., Zhao Z., and Tian M., “Shortterm Wind Speed Forecasting Based on a Hybrid Model,” Applied Soft Computing, vol. 13, no. 7, pp. 3225-3233, 2013. https://doi.org/10.1016/j.asoc.2013.02.016