From Local Winds to Global Goals: The Role of Wind Speed Forecasting in Achieving Renewable Energy Sustainability in Kerala

From Local Winds to Global Goals: The Role of Wind Speed Forecasting in Achieving Renewable Energy Sustainability in Kerala

Authors

  • Mr. Muhammed Anees V. V.
  • K. P. Abdul Nazar
  • Sajeeb Ayamannil

DOI:

https://doi.org/10.58213/vidhyayana.v10isi3.2232

Keywords:

Wind Speed Forecasting, Renewable Energy Sustainability, Kerala Renewable Energy, Wind Energy Optimization, Machine Learning in Renewable Energy, Explainable AI, Energy Grid Integration

Abstract

Forecasting wind speed is essential for improving wind energy systems and attaining renewable energy sustainability. As global energy transitions accelerate, precise forecasting methodologies are essential for maintaining the efficiency and dependability of wind energy plants. This research examines the methodology, difficulties, and opportunities associated with wind speed forecasting, specifically in Kerala, India. Kerala's distinctive geographic and meteorological diversity makes it an intriguing subject for analysing the regional complexities of wind energy development. The state exhibits considerable wind variability due to monsoon patterns, coastal geography, and hilly terrains, necessitating specialized and sophisticated forecasting methods. This study examines diverse statistical, machine learning, and hybrid forecasting models and their relevance in the context of Kerala, tackling issues related to data scarcity, seasonal variability, and grid integration. It emphasizes the significance of predicting in enhancing wind farm operations during peak monsoon seasons and strategizing for low-output intervals. Moreover, Kerala's dependence on renewable energy sources, including hydro and solar, highlights the necessity for wind energy diversification to improve energy security and resilience.

The document underscores the significance of legislative frameworks and community involvement in promoting the adoption of wind energy solutions. This research integrates worldwide achievements with Kerala-specific insights to identify strategies for addressing regional difficulties and harnessing the state's latent wind energy potential. Stakeholders, including legislators, energy planners, and researchers, are offered practical advice to utilize wind speed forecasting as a mechanism for sustainable energy transitions. The study connects local renewable energy ambitions with global environmental objectives, establishing Kerala as a paradigm for creative wind energy approaches in analogous places globally.

Downloads

Download data is not yet available.

References

Almeida, A., & Marques, A. (2019). A hybrid ARIMA-neural network model for short-term wind power forecasting. Applied Energy, 238, 269-278. https://doi.org/10.1016/j.apenergy.2019.01.045

Bacher, P., Madsen, H., & Nielsen, H. A. (2012). Short-term forecasting of wind power production using an ensemble of models. Wind Energy, 15(3), 247-257. https://doi.org/10.1002/we.480

Boulanger, P., & Liang, Q. (2017). Wind forecasting using deep learning models for optimal wind turbine operation. Energy Conversion and Management, 148, 879-889. https://doi.org/10.1016/j.enconman.2017.06.026

Boudraa, D., & Nakhli, A. (2013). Wind power prediction using hybrid models for grid integration. Energy Conversion and Management, 73, 78-85. https://doi.org/10.1016/j.enconman.2013.04.029

Brun, K., & Ovsthus, S. (2018). Integration of renewable energy sources into smart grids using wind forecasting data. Renewable Energy, 119, 48-56. https://doi.org/10.1016/j.renene.2017.12.031

Elayadi, A., & Goudarzi, H. (2019). Forecasting wind speed using ARIMA and neural networks for renewable energy applications. Journal of Energy Engineering, 145(6), 04019027. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000662

Ghosh, S., & Ray, S. (2018). Hybrid machine learning approaches for wind power forecasting. Energy Reports, 4, 441-451. https://doi.org/10.1016/j.egyr.2018.02.006

Gupta, A., & Agarwal, A. (2020). Wind speed forecasting using deep learning models: A comprehensive review. Renewable and Sustainable Energy Reviews, 130, 109875. https://doi.org/10.1016/j.rser.2020.109875

He, H., & Wang, Y. (2014). An improved wind power prediction model based on Support Vector Machine (SVM) and particle swarm optimization. Energy Conversion and Management, 78, 1037-1047. https://doi.org/10.1016/j.enconman.2013.11.015

Lopez, J., Campo, M., & Saez, M. (2017). A comprehensive review of wind forecasting models. Renewable and Sustainable Energy Reviews, 76, 28-44. https://doi.org/10.1016/j.rser.2017.03.062

Li, W., & Zhang, L. (2017). Wind speed prediction using deep neural networks: A review and comparative study. Energy Reports, 3, 292-306. https://doi.org/10.1016/j.egyr.2017.04.002

Nicolás, G., & Gaillard, J. M. (2014). Application of wind power forecasting for grid management. IEEE Transactions on Power Systems, 29(2), 561-569. https://doi.org/10.1109/TPWRS.2013.2263952

Pereira, J. R., & Andrade, P. (2019). Forecasting short-term wind power using hybrid wavelet transform and machine learning models. Journal of Renewable and Sustainable Energy, 11(4), 043304. https://doi.org/10.1063/1.5093474

Ranjan, R., & Rao, P. (2018). Wind energy prediction using hybrid machine learning models. Renewable and Sustainable Energy Reviews, 82, 1304-1321. https://doi.org/10.1016/j.rser.2017.09.069

Saha, S., & Shah, N. (2020). Wind speed prediction using hybrid deep learning model and statistical analysis. Renewable Energy, 158, 1107-1120. https://doi.org/10.1016/j.renene.2020.05.017

Soman, S. S., & Venkatesh, R. (2015). Wind power forecasting: A review of techniques. Energy, 80, 192-205. https://doi.org/10.1016/j.energy.2014.12.020

Tang, X., & Yang, J. (2019). A hybrid forecasting model based on machine learning for wind speed prediction. Energy Reports, 5, 799-806. https://doi.org/10.1016/j.egyr.2019.06.004

Wang, X., & Liu, C. (2016). A review of wind speed forecasting models. Journal of Wind Engineering and Industrial Aerodynamics, 159, 38-54. https://doi.org/10.1016/j.jweia.2016.06.009

Wu, Y., & Liu, H. (2019). Long-term wind power forecasting using an LSTM neural network. Renewable Energy, 138, 1060-1071. https://doi.org/10.1016/j.renene.2019.02.054

Yang, W., & Han, L. (2020). A comprehensive study on wind speed prediction using machine learning algorithms. Energy Reports, 6, 443-449. https://doi.org/10.1016/j.egyr.2020.03.014

Zhang, S., & Cheng, M. (2018). A hybrid model combining wavelet transform and extreme learning machine for short-term wind speed forecasting. Energy, 158, 122-131. https://doi.org/10.1016/j.energy.2018.06.086

Zhang, X., & Ma, Z. (2019). A hybrid wavelet transform and extreme gradient boosting model for wind speed forecasting. Renewable Energy, 138, 276-287. https://doi.org/10.1016/j.renene.2019.02.013

Zhao, H., Liu, X., & Lu, X. (2015). A hybrid model based on Support Vector Regression and particle swarm optimization for wind speed forecasting. Renewable Energy, 75, 89-100. https://doi.org/10.1016/j.renene.2014.09.043

Zhou, J., Yu, L., & Yang, X. (2021). Wavelet-transform-based hybrid model for wind speed forecasting. Energy, 224, 120065. https://doi.org/10.1016/j.energy.2021.120065 achieving energy sustainability.

Additional Files

Published

25-02-2025

How to Cite

Mr. Muhammed Anees V. V., K. P. Abdul Nazar, & Sajeeb Ayamannil. (2025). From Local Winds to Global Goals: The Role of Wind Speed Forecasting in Achieving Renewable Energy Sustainability in Kerala . Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 10(si3). https://doi.org/10.58213/vidhyayana.v10isi3.2232
Loading...