| Title |
Morphing to the Mean Approach of Anticipated Electricity Demand in Smart City Partitions Using Citizen Elasticities |
| ID_Doc |
38242 |
| Authors |
Alamaniotis, M |
| Title |
Morphing to the Mean Approach of Anticipated Electricity Demand in Smart City Partitions Using Citizen Elasticities |
| Year |
2018 |
| Published |
|
| DOI |
|
| Abstract |
This paper frames itself in the information rich environment of a smart city where residents can form groups to pursue a common goal. Those groups that consist of partitions of the smart city, have as a goal, among others, to smooth the aggregated electricity demand of the residents, thus, contributing to the stability of the power grid. In the current work, a new approach called morphing to the mean is presented that aims at morphing the overall electricity demand curve associated with the partition; morphing refers to smoothing out the anticipated demand curve and minimizing the demand fluctuation using as a baseline the mean demand value. To that end, the proposed methodology engages the cascading use of individual resident demand elasticities and genetic algorithms to attain an acceptable solution. Obtained results demonstrate the efficiency of the methodology in smoothing demand curve of a smart city partition. |
| Author Keywords |
smart cities; elasticities; genetic algorithms; demand anticipation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000851592100095 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Green & Sustainable Science & Technology; Engineering, Multidisciplinary |
| Research Area |
Computer Science; Science & Technology - Other Topics; Engineering |
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