Design and implementation of privacy preserving billing protocol for smart grid
The Journal of Supercomputing
| Hi-index | 0.43 |
Providing Feedback of power consumption by using Advanced Metering Infrastructure (AMI) and smart meters is considered the best method to lower consumers' power consumption. Automatic analysis of power consumption data that provides information on user activity is essential in order to provide effective feedback in time, and advanced services such as automatic power load control based on context information. The activity should be matched with user's behavior in order to make it understandable for the user and readable for the machine. In this paper, a new method for extracting activities from power consumption data is proposed. By using the concept of activities in daily living (ADLs), the method extracts the activities as tasks from context information such as identification and location of electric appliances and temporal power consumption from the AMI and the smart meters. Using ontology, the tasks are tagged by semantic meta-information in order to be used for sophisticated user friendly services.