# Advanced neural network and hybrid models for wind power forecasting: a comprehensive global review

### Corresponding Author(s) : Bongumsa Mendu

*Future Energy*,
Vol. 3 No. 4 (2024): November 2024 Issue

## Abstract

Neural Network Algorithms (NNAs), modeled after the workings of biological neurons, are increasingly utilized in areas like data mining and robotics to address complex challenges in artificial intelligence (AI). This research will undertake a systematic review based on advanced neural networks and hybrid models for wind power forecasting. Using the Scopus database, a methodical search, acquisition, and filtering procedure was utilized to find pertinent publication documents; VOSviewer software was utilized to analyze trends. The emphasis on improving prediction accuracy and stability in wind power forecasting through the application of cutting-edge machine learning techniques and hybrid models is a prominent feature that unites the literature. Furthermore, attention is being paid to resolving issues pertaining to the production of wind energy, such as wind power fluctuation management, grid integration problems, wind speed prediction, and turbine health monitoring. A rising trend involves multi-dimensional, multi-step forecasting and incorporating factors like weather data and spatial-temporal features to enhance reliability. This paper contributes by exploring the integration of optimization techniques with neural networks, investigating hybrid models to improve wind power predictions, assessing LSTM-based approaches in forecasting, and suggesting directions for future research.

## Keywords

*Future Energy*3 (4):67-79. https://fupubco.com/fuen/article/view/211.

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#### References

Amey T, Archit K, “Fundamentals of Neural Networks”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9 (8), 2021, pp. 407-426.

Ashley H, Eric L, Roland L, “A new neural network feature importance method: Application to mobile robots controllers gain tuning”, ICINCO 2020, 17th International Conference on Informatics in Control, Automation and Robotics, Jul 2020, Paris, France, pp. 1-8.

Rifky L.R, M. Sudarma, “Application of Neural Network Overview In Data Mining”, International Journal of Engineering, vol. 2, 2017, pp. 1-94.

O. Nerrand, P. Roussel-Ragot, L. Personnaz, G. Dreyfus and S. Marcos, “Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms”, Neural Computation, vol. 5 (2), 1993, pp. 165-199.

Salvatore G, Maria G.X, “Innovative Topologies and Algorithms for Neural Networks”, Future Internet, vol. 12 (7), 2020, pp. 1-4.

Abdulkadirov R., Lyakhov P., Nagornov N, “Survey of Optimization Algorithms in Modern Neural Networks”, Mathematics, vol. 11 (11), 2023, pp. 1-37.

Eng. Nawaf Mohammad H Alamri, “A Review on Neural Network and Deep Learning”, International Journal of Current Engineering and Technology, vol.10 (5), 2020, pp. 734-739.

A. Doroshenko, “Applying Artificial Neural Networks in Construction”, E3S web of Conferences, vol. 143, 2020, pp. 1-4.

E. Vonk, L.P.J. Veelenturf, “Neural Networks: Implementations and Applications”, EEE Aerospace and Electronic Systems Magazine, vol. 11 (7), 1996, pp. 11-16.

] Chirag, “Overview of Neural Network”, International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 2 (5), 2022, pp. 531-534.

Subhash K.S, Shikha S, “An Overview on Neural Network and Its Application”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 9 (8), 2021, pp. 1242-1248.

Fabiyi Samson Damilola. “A review of unsupervised artificial neural networks with applications”, International Journal of Computer Applications, vol. 181(40), 2019, pp. 22-26.

] Bell Raj Eapen, “‘Neural network’ algorithm to predict severity in epidermolysis bullosa simplex”, Indian Journal of Dermatol, Venereol and Leprol, vol. 71, 2005, pp. 106-108.

Baida Abdulredha Hamdan, “Neural Network Principles and its Application”, Webology, vol. 19 (1), 2022, pp. 3955-3970.

W. E. Leithead, “Wind Energy”, Philosophical Transactions: Mathematical, Physical and Engineering Sciences, vol. 365 (1853), 2007, pp. 957–970.

Samah A. Gamel, Yara A. Sultan, “Winds of Power: Data Analysis for the Relationship between Wind Speed, Gust, and Power Output”, Journal of Engineering Research (ERJ), vol. 7 (5), 2023, pp. 189-194.

Upma Singh, Mohammad Rizwan, Hasmat Malik, Fausto Pedro García Márquez, “Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review”, Energies, vol. 15 (6), 2022, pp.1-39.

Lingling B, Haiyang P, Li He, Jijian L, “An Importance Analysis–Based Weight Evaluation Framework for Identifying Key Components of Multi-Configuration Off-Grid Wind Power Generation Systems under Stochastic Data Inputs”, Energies, vol. 12 (22), 2019, pp. 1-22.

Saad Bin A.K, Muhammad E. H. C, Amith K, Jubaer A, Azad A, Nushrat S, “Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations”, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11(5), 2020, pp. 1-18.

Song Y. D., Li P., Liu W., and Qin M, “An overview of renewable wind energy conversion system modeling and control”, Measurement and Control, vol. 43(7), 2010, pp. 203-208.

Zhang Xingguo, Liu Shuhua, Huang Liang, Wu Hui, “The Current Status and Development Trend of Wind Power Generation”, 7th International Conference on Advanced Materials and Computer Science, Dalian, 2018, pp. 160-166.

Fakhriddin S. K, Dilshodbek Ravshanbek O’g’li Xusanov, Fazliddin Xusanboy O’g’li Ashuraliyev, “The Importance of Wind Energy and its reasonable usage”, Current Research Journal of Pedagogics, vol. 4 (4), 2023, pp. 58-64.

Shin H., Rüttgers M., Lee S, “Neural Networks for Improving Wind Power Efficiency: A Review”, Fluids, vol. 7 (12), 2022, pp. 1-16.

Alkabbani H, Ahmadian A, Zhu Q, Elkamel A, “Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review”, Frontiers in Chemical Engineering, vol. 3, 2021, pp. 1-21.

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