Predicting Wind Energy Resources and Minimizing its Effects on Birds

Choudhary, Arunendra (2022) Predicting Wind Energy Resources and Minimizing its Effects on Birds. Masters thesis, Dublin, National College of Ireland.

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Abstract

Recently, the world has seen widespread adoption of renewable energy resources. This can be achieved by using a variety of renewable energy sources, out of which wind energy is the most popular all over the world. Fossil fuels emit a lot of carbon emissions which contributes to global warming and nowadays they are expensive too. Future energy sources are renewal energy which will be the primary focus of energy predictions. Over the past decade, wind energy generation has gradually increased with the help of machine learning technologies which are constantly developing. Wind energy prediction is the main task because the energy supply-demand should be maintained by the energy grid if nations are moving toward global energy. Time series analysis will help to achieve wind energy prediction goals. On the one hand, wind turbines provide a renewable source of energy, but on the other hand, while the blades are in operation, several bird species collide with them and die, doing significant harm to the bird species. Therefore, This research work focuses on developing novel solutions for wind energy predictions and bird life preservation. In this work, we will integrate wind energy prediction with bird identification to better understand wind energy prediction and protect bird life.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QL Zoology T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	17 May 2023 16:31
Last Modified:	17 May 2023 16:31
URI:	https://norma.ncirl.ie/id/eprint/6580

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