Internet of Things (IOT) Based Machine Learning Techniques for Wind Energy Harvesting
R. Kalpana, Vinitha Hannah Subburaj, R. Lokanadham, K. Amudha, G. N. Beena Bethel, Arvind Kumar Shukla, Pravin R. Kshirsagar, A. Rajaram
Abstract
The Internet of Things (IoT) is a significant avenue for research in renewable energy, particularly in enhancing windmill performance, reducing wind energy costs, and mitigating risks in wind power. This article concentrates on leveraging IoT for assessing wind and solar energy, as well as estimating module lifespans. IoT has improved assessment methods, monitoring precision, and product testing, influencing power network reliability and inventory management in green energy. Predicting green energy output is crucial but challenging due to wind speed fluctuations. Machine learning (ML) techniques are applied to predict wind-based electricity output, with a comparative evaluation of forecasting methods. IoT technologies and algorithms enable energy consumption forecasts, yielding more accurate predictions and lower root mean square error (RMSE). Accurate meteorological forecasts are paramount in the green energy sector, necessitating predictive models for authentic wind generator data. The research aims to develop technologies for precise forecasts, with a focus on comprehensive wind forecast algorithms for photovoltaic systems. Various ML techniques and green energy prediction software are assessed for their accuracy in this endeavor.