Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Managing end-user preferences in the smart grid
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
The smart thermostat: using occupancy sensors to save energy in homes
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
nPlug: a smart plug for alleviating peak loads
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Minimizing intrusiveness in home energy measurement
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Towards the automated extraction of flexibilities from electricity time series
Proceedings of the Joint EDBT/ICDT 2013 Workshops
TESLA: an energy-saving agent that leverages schedule flexibility
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
TESLA: an extended study of an energy-saving agent that leverages schedule flexibility
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.01 |
Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity taris. In this work, we present Yupik, a system that helps users respond to real-time electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer user's preferred usage profile. Using the preferred profile as a reference, Yupik's optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupik's capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices.