| Title |
Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks |
| ID_Doc |
41222 |
| Authors |
Garcia, FCC; Macabebe, EQB |
| Title |
Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks |
| Year |
2017 |
| Published |
|
| DOI |
10.1145/3132847.3133146 |
| Abstract |
E nergy management presents one of the principal sustainability challenges within urban centers given that they account for 75% of the energy consumption worldwide. In the context of a smart city framework, the use of intelligent urban systems provides a key opportunity in addressing the energy sustainability issue as an informatics problem where the goal is to deliver energy usage feedback to the users as a means of enabling behavioral change towards energy sustainability. In this paper we present a method to provide appliance energy usage feedback from smart meters using energy disaggregation. We put energy disaggregation in the context of a source separation and signal reconstruction problem in which we train a fully convolutional encoder decoder network to separate appliance energy usage from aggregate whole house electricity consumption data. The results show that the proposed fully convolutional encoder decoder model can achieve competitive accuracy compared with several state-of-the-art methods. |
| Author Keywords |
Energy Disaggregation; Deep Learning; Ambient Intelligence; Energy Management; Smart City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000440845300222 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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