Optimizing Wind Energy Generation: Wind Speed Forecasting Using Elman Recurrent Neural Networks for Enhanced Power Generation in Turbines
DOI:
https://doi.org/10.35335/zgnvvy88Keywords:
Elman Recurrent Neural Network, Power Generation, Renewable Energy Optimizations, Wind Speed Forecasting, Wind TurbinesAbstract
This research delves into the realm of wind energy by exploring the accuracy of wind speed forecasting using the Elman Recurrent Neural Network (RNN) and its direct influence on the power generation of wind turbines. Leveraging historical wind speed data and employing the Elman RNN, this study demonstrates the model's precision in forecasting wind speeds, capturing temporal dependencies, and elucidating their impact on electricity output. Correlating these forecasts with actual power generation records, the research establishes a profound relationship, showcasing how even minor variations in predicted wind speeds significantly influence the amount of electricity produced by wind turbines. The study's findings underscore the critical role of accurate wind speed predictions in optimizing wind farm operations, enhancing energy capture efficiency, and contributing to grid stability. Furthermore, the research sets the stage for practical applications in renewable energy planning and policy-making, offering insights that shape the future trajectory of wind energy utilization. The research concludes by proposing avenues for further refinement in predictive models, real-time integration strategies, and long-term forecasting, guiding the path towards a sustainable and resilient energy landscape.References
Bakir, I., Yildirim, M., & Ursavas, E. (2021). An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance. Renewable and Sustainable Energy Reviews, 138, 110639.
Burton, T., Jenkins, N., Sharpe, D., & Bossanyi, E. (2011). Wind energy handbook. John Wiley & Sons.
Cai, H., Jia, X., Feng, J., Li, W., Hsu, Y.-M., & Lee, J. (2020). Gaussian process regression for numerical wind speed prediction enhancement. Renewable Energy, 146, 2112–2123.
Coen, J. (2018). Some requirements for simulating wildland fire behavior using insight from coupled weather—Wildland fire models. Fire, 1(1), 6.
Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable Energy, 37(1), 1–8.
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50.
Gipe, P. (2018). Wind energy for the rest of Us: a Comprehensive Guide to wind power and how to Use it. Wind-works. org.
Kumar, Y., Ringenberg, J., Depuru, S. S., Devabhaktuni, V. K., Lee, J. W., Nikolaidis, E., Andersen, B., & Afjeh, A. (2016). Wind energy: Trends and enabling technologies. Renewable and Sustainable Energy Reviews, 53, 209–224.
Li, F., Ren, G., & Lee, J. (2019). Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks. Energy Conversion and Management, 186, 306–322.
Lipu, M. S. H., Miah, M. S., Hannan, M. A., Hussain, A., Sarker, M. R., Ayob, A., Saad, M. H. M., & Mahmud, M. S. (2021). Artificial intelligence based hybrid forecasting approaches for wind power generation: Progress, challenges and prospects. IEEE Access, 9, 102460–102489.
Liu, H., Yu, C., Wu, H., Duan, Z., & Yan, G. (2020). A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting. Energy, 202, 117794.
Mac Kinnon, M. A., Brouwer, J., & Samuelsen, S. (2018). The role of natural gas and its infrastructure in mitigating greenhouse gas emissions, improving regional air quality, and renewable resource integration. Progress in Energy and Combustion Science, 64, 62–92.
Masterson, C. D. (2009). Wind-Energy Ventures in Indian Country: Fashioning a Functional Paradigm. Am. Indian L. Rev., 34, 317.
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., & Conzelmann, G. (2009). Wind power forecasting: State-of-the-art 2009. Argonne National Lab.(ANL), Argonne, IL (United States).
Njiri, J. G., & Söffker, D. (2016). State-of-the-art in wind turbine control: Trends and challenges. Renewable and Sustainable Energy Reviews, 60, 377–393.
Oegren, M., & Kropp, W. (2004). Road traffic noise propagation between two dimensional city canyons using an equivalent sources approach. Acta Acustica United with Acustica, 90(2), 293–300.
Pasqualetti, M. J. (2000). Morality, space, and the power of wind‐energy landscapes. Geographical Review, 90(3), 381–394.
Rai, R., & Sahu, C. K. (2020). Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus. IEEE Access, 8, 71050–71073.
Saint-Drenan, Y.-M., Besseau, R., Jansen, M., Staffell, I., Troccoli, A., Dubus, L., Schmidt, J., Gruber, K., Simões, S. G., & Heier, S. (2020). A parametric model for wind turbine power curves incorporating environmental conditions. Renewable Energy, 157, 754–768.
Shafiullah, G. M., Oo, A. M. T., Ali, A. B. M. S., & Wolfs, P. (2013). Potential challenges of integrating large-scale wind energy into the power grid–A review. Renewable and Sustainable Energy Reviews, 20, 306–321.
Tascikaraoglu, A. (2018). Evaluation of spatio-temporal forecasting methods in various smart city applications. Renewable and Sustainable Energy Reviews, 82, 424–435.
Tascikaraoglu, A., & Uzunoglu, M. (2014). A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34, 243–254.
Vargas, S. A., Esteves, G. R. T., Maçaira, P. M., Bastos, B. Q., Oliveira, F. L. C., & Souza, R. C. (2019). Wind power generation: A review and a research agenda. Journal of Cleaner Production, 218, 850–870.
Vasi, I. B. (2011). Winds of change: The environmental movement and the global development of the wind energy industry. Oxford University Press.
Xu, J., & Liu, T. (2020). Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development. Energy Policy, 142, 111538.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Anthony Brandon, Richrad Xavier, Cley Valentine Ludwig (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Vertex is licensed under a 