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Social Networking System to Reduce Peak Energy Consumption in a Smart Grid

Detailed Technology Description

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*Abstract

Social Network Users Share Electricity Consumption Habits to Reduce Energy Costs for Consumers and Utility Companies

This social networking platform helps users schedule electrical appliance tasks, minimizing variance in power consumption and saving money. Energy consumption in buildings represents approximately 74 percent of the nation’s electricity consumption. During peaks in electricity demand, generators are inefficient, wasting fuel and increasing expenditures for both electric companies and end-users. Electric companies keep generators on, ready to respond to sudden upswings in electricity consumption. These inefficiencies cost billions of dollars in fuel and force electric companies to charge households more at peak consumption times. University of Florida researchers have developed a social network that uses an algorithm to decrease peak power consumption across a smart grid by distributing scheduled jobs of socially connected user clusters. The algorithm determines the usage patterns of users and schedules electrical tasks to achieve minimum variance in power consumption. This results in better performance in terms of user payments, peak power consumption and fuel costs.

Application

A clustered social network that logs electricity usage trends to help minimize peak energy consumption and maximize savings for the user and utility company

Advantages

• Offers family or social network plans, reducing peak power consumption
• Uses a distributed clustering scheme, providing an unprecedented scalability for a large network
• Is capable of simulating electricity usage data for an unlimited number of households, making it practical to evaluate performance of techniques

Technology

The system constructs "social graphs" of power consumption based on user-defined groups. Given a social graph, the algorithm aims at finding clusters of users that achieve minimum variance in power consumption throughout the day. Clusters are formed by sending energy usage patterns of at least one user to another within a social network and scheduling at least one job for the first user to reduce peak power used within the cluster.

*Principal Investigator

Name: Dapeng Wu

Department:

Name: Jing Zhao

Department:

Name: Qiuyuan Huang

Department:

Name: Xiang-Yang Li

Department:

Name: Xin Li

Department:

Country/Region

USA