| Title |
Renewable Green Energy Resources for Next-Generation Smart Cities Using Big Data Analytics |
| ID_Doc |
44662 |
| Authors |
Zhu, LL; Shi, JF; Shi, YH; Xu, HP; Shanthini, A; Seetharam, TG |
| Title |
Renewable Green Energy Resources for Next-Generation Smart Cities Using Big Data Analytics |
| Year |
2022 |
| Published |
Journal Of Interconnection Networks, 22, Supp01 |
| DOI |
10.1142/S0219265921410048 |
| Abstract |
Energy is now seen as a significant resource that develops abundant on the world economy, with short supply and development. A study found that renewable energy systems are needed to prevent shortages. Hence, all the focus in this study to decrease electricity consumption and reduce the overall completion times for a regular console in green technology networks was an efficient and scalable production genomic solution. A Renewable green energy resources smart city (RGER-SC) framework is proposed that used a multi-target evolutionary algorithm was hybridized to be effective and calculated arithmetically in this study. This work deals with fostering renewable energy incorporation by adjusting federal charges to increase the energy accounting practitioners. Besides, this report analyses the timely generation of delay-tolerant demands and the maintenance of district heating at network infrastructure. In comparison, capacity differentials between consumers and information centres are considered and evaluated using the Renewable green energy resources smart city (RGER-SC) framework for energy conservation and controlled task management at an industrial level. |
| Author Keywords |
Renewable energy; smart cities; big data analytics; green energy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000867635500006 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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