| Title |
Bigdata Assisted Energy Conversion Model for Innovative City Application |
| ID_Doc |
42125 |
| Authors |
Zhu, J; Vadivel, T; Sivaparthipan, CB |
| Title |
Bigdata Assisted Energy Conversion Model for Innovative City Application |
| Year |
2022 |
| Published |
Journal Of Interconnection Networks, 22, Supp01 |
| DOI |
10.1142/S0219265921410085 |
| Abstract |
Centrally controlled energy conversion schemes in intelligent residential microgrids are a difficult optimization challenge because of their range of processing and power devices accessible. Typical steps to shrink the weight and seriousness of the issues are decreasing modelling precision, adding several weights, or adjusting the measurement accuracy. Nevertheless, because these interventions modify the specialization issue and thus result in various approaches as expected, this article introduces a Bigdata assisted energy conversion model (BD-ECM) and evaluates a decomposition approach to solve the initial problem recursively. Compared to the initial compact version, the decayed approach is tested to demonstrate that all versions differ less than 18.8%. Moreover, both methods contribute to the use of roughly similar structures. The results reveal that because of the existing constraints on computational capital and simulation techniques, condensed development of the common law can only be extended to moderate and limited intelligent grids. However, decentralized approaches can be dealing with sizeable dispersed generation structures. To assess the month's environmental and strategic advantages as part of the system, researchers extend the decompiled approach to a massive smart grid. The data reveal that prices can be lowered by 14.0% in local energy exchanges and pollution by 23.9% in the situation studied. |
| Author Keywords |
Smart city; energy conversation; big data; energy system |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000867635500021 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
|