ViridiScope: design and implementation of a fine grained power monitoring system for homes
Proceedings of the 11th international conference on Ubiquitous computing
Experiences with a high-fidelity wireless building energy auditing network
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Energy efficient building environment control strategies using real-time occupancy measurements
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Occupancy based demand response HVAC control strategy
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Automating energy management in green homes
Proceedings of the 2nd ACM SIGCOMM workshop on Home networks
Principles of smart home control
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Minimizing intrusiveness in home energy measurement
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Proceedings of the fourth international conference on Future energy systems
Sharing renewable energy in smart microgrids
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
Incentivizing Advanced Load Scheduling in Smart Homes
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Distributed generation (DG) uses many small on-site energy sources deployed at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to many homes is difficult. In this paper, we explore a different approach that combines residential TOU pricing models with on-site renewables and modest energy storage to incentivize DG. We propose a system architecture and control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual buildings. We evaluate our control algorithm by simulation using a collection of real-world data sets. Initial results show that the algorithm decreases grid power costs by 2.7X while nearly eliminating grid demand peaks, demonstrating the promise of our approach.