A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
User-sensitive scheduling of home appliances
Proceedings of the 2nd ACM SIGCOMM workshop on Green networking
RealTime distributed congestion control for electrical vehicle charging
ACM SIGMETRICS Performance Evaluation Review
Deep conservation in urban India and its implications for the design of conservation technologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scaling distributed energy storage for grid peak reduction
Proceedings of the fourth international conference on Future energy systems
DC picogrids: a case for local energy storage for uninterrupted power to DC appliances
Proceedings of the fourth international conference on Future energy systems
Hidden costs of power cuts and battery backups
Proceedings of the fourth international conference on Future energy systems
Distributed control of electric vehicle charging
Proceedings of the fourth international conference on Future energy systems
How to auto-configure your smart home?: high-resolution power measurements to the rescue
Proceedings of the fourth international conference on Future energy systems
Incentivizing Advanced Load Scheduling in Smart Homes
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
It's Different: Insights into home energy consumption in India
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.