Abstract
Low-energy technologies in big data applications in remote areas are still unable to offer appropriate level of reliable environment. So it is of utmost importance to meet the energy consumption of Internet of Things (IoT) using renewable energy sources. Wind energy is a vital substitute of traditional fossil fuel due to better efficiency, cost and reliability, and better contribution for sustainable development in future energy generation all over the world. This study presents an artificial neural networks (ANN) based model of power output of wind energy harvesting system in relation with affecting factors such as wind velocity, air density, air pressure, temperature, and relative humidity to meet energy requirements of large scale big data applications in remote areas.
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Authors would like to thank the researchers or academicians whose works have been cited in this review paper. Authors are also grateful to Punjabi University Patiala for offering sufficient library and internet facility and ministry of education of Ethiopia for financial support.
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Mitiku, T., Manshahia, M.S. (2020). Artificial Neural Networks Based Green Energy Harvesting for Smart World. In: Somani, A.K., Shekhawat, R.S., Mundra, A., Srivastava, S., Verma, V.K. (eds) Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-13-8406-6_4
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DOI: https://doi.org/10.1007/978-981-13-8406-6_4
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