Version 1
: Received: 18 November 2022 / Approved: 21 November 2022 / Online: 21 November 2022 (09:53:30 CET)
How to cite:
Duraipandy, P.; Prabhu, A.V.; Alshehri, M.; Routray, S.; Al Hamyani, H.A.R.A.; Alowaidi, M.; L, S.K. A Novel Energy Optimization System for Renewable Demand using Heuristics, Distributed Systems, and Machine Learning in a Smart City. Preprints2022, 2022110387. https://doi.org/10.20944/preprints202211.0387.v1
Duraipandy, P.; Prabhu, A.V.; Alshehri, M.; Routray, S.; Al Hamyani, H.A.R.A.; Alowaidi, M.; L, S.K. A Novel Energy Optimization System for Renewable Demand using Heuristics, Distributed Systems, and Machine Learning in a Smart City. Preprints 2022, 2022110387. https://doi.org/10.20944/preprints202211.0387.v1
Duraipandy, P.; Prabhu, A.V.; Alshehri, M.; Routray, S.; Al Hamyani, H.A.R.A.; Alowaidi, M.; L, S.K. A Novel Energy Optimization System for Renewable Demand using Heuristics, Distributed Systems, and Machine Learning in a Smart City. Preprints2022, 2022110387. https://doi.org/10.20944/preprints202211.0387.v1
APA Style
Duraipandy, P., Prabhu, A.V., Alshehri, M., Routray, S., Al Hamyani, H.A.R.A., Alowaidi, M., & L, S.K. (2022). A Novel Energy Optimization System for Renewable Demand using Heuristics, Distributed Systems, and Machine Learning in a Smart City. Preprints. https://doi.org/10.20944/preprints202211.0387.v1
Chicago/Turabian Style
Duraipandy, P., Majed Alowaidi and Satish Kumar L. 2022 "A Novel Energy Optimization System for Renewable Demand using Heuristics, Distributed Systems, and Machine Learning in a Smart City" Preprints. https://doi.org/10.20944/preprints202211.0387.v1
Abstract
Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.
Keywords
renewable energy, Internet of Thinks, renewable energy storage, smart city
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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