Deep Neural Networks for Future Low Carbon Energy Technologies: Potential, Challenges and Economic Development

Asif, Rameez (2020) Deep Neural Networks for Future Low Carbon Energy Technologies: Potential, Challenges and Economic Development. In: Proceedings - 2020 IEEE 6th International Conference on Big Data Computing Service and Applications, BigDataService 2020. Proceedings - 2020 IEEE 6th International Conference on Big Data Computing Service and Applications, BigDataService 2020. IEEE, pp. 136-141. ISBN 978-1-7281-7022-0

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Abstract

The global energy demands are growing every year, and fossil fuels won't be able to fulfill our energy needs in the near future. Carbon emissions from the fossil fuels hit an all-time high in 2018 due to increased energy consumption around the globe. On the other hand, renewable energy is an emerging technology and considered as a reliable alternative to the fossil fuels. It is much safer and cleaner than conventional sources. With the advancements in technology, the renewable energy sector has made significant progress in the last decade. One most significant challenge, the large scale renewable energy farms are facing is the un-predictability of the weather patterns. This stochastic nature of the weather data is impacting the solar and wind farms significantly. Although, the classical technologies are in place for weather forecasting but they are not efficient enough to give a feedback to the base-station for any sudden change or future predictions. The demand for renewable energy will only increase in the future. And, that is why renewable energy companies need to invest in Artificial Intelligence (AI), Internet-of-Things (IoT), and other emerging technologies to improve productivity and overcome the shortfalls. Even the large consumers of renewable energy, like supermarkets, factories, offices, railways can use AI technology to make data-driven decisions on power usage and demand. In this article, we present an overview of AI techniques for modelling, prediction and forecasting of wind farming data. Additionally, we have presented economic impact of low carbon energy techniques by analysing the climate change patterns and diverse sources of power generation for the Scotland, United Kingdom region, as a case study.

Item Type: Book Section
Uncontrolled Keywords: software,information systems and management,artificial intelligence,information systems,computer science applications,sdg 7 - affordable and clean energy,sdg 8 - decent work and economic growth,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 26 Jan 2022 11:30
Last Modified: 10 May 2022 00:05
URI: https://ueaeprints.uea.ac.uk/id/eprint/83156
DOI: 10.1109/BigDataService49289.2020.00028

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