A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

Many electricity suppliers around the world are deploying smart meters to gather fine-grained spatiotemporal consumption data and to effectively manage the collective demand of their consumer base. In this paper, we introduce a structured framework and a discriminative index that can be used to segment the consumption data along multiple contextual dimensions such as locations, communities, seasons, weather patterns, holidays, etc. The generated segments can enable various higher-level applications such as usage-specific tariff structures, theft detection, consumer-specific demand response programs, etc. Our framework is also able to track consumers’ behavioral changes, evaluate different temporal aggregations, and identify main characteristics which define a cluster.

Rapid depletion of fossil fuels and water resources has become an international problem. Urban residential households are among the primary consumers of resources and are deeply affected by resource shortages. Despite the global nature of these problems, most of the solutions being developed to address these issues are based on studies done in the developed world. We present a study of energy, water and fuel conservation practices in urban India. Our study highlights a culture of deep conservation and the results raise questions about the viability of typical solutions such as home energy monitors. We identify new opportunities for design such as point-of-use feedback technologies, modular solutions, distributed energy storage, harnessing by-products and automated load shifting.

Though rapid depletion of natural resources has become a global problem, most of the solutions developed to address it are based on studies done in the developed world. Moreover, the commercial sector is among the primary consumers of resources, yet research work has been mostly limited to residential users. We present a study exploring employees' perception, their beliefs and attitudes, towards environmental sustainability at workplaces in a developing region. To obtain broader context, we also conducted a focus group with the facility team members. Our study highlights that in spite of strong motivations to conserve, employees conservative actions are limited due to lack of controls, knowledge and responsibility. We identify new opportunities for design such as designing location specific buildings, removing inefficient choices, and building communal spaces, to facilitate conservation at workplaces.

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 Demand Side Management (DSM) strategies may not be suitable for India as the local conditions usually favor inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present a completely autonomous DSM controller called the nPlug. nPlug is positioned between the wall socket and deferrable load(s) such as water heaters, washing machines, and electric vehicles. nPlugs combine local sensing and analytics to infer peak periods as well as supply-demand imbalance conditions. They schedule attached appliances in a decentralized manner to alleviate peaks whenever possible without violating the requirements of consumers. nPlugs do not require any manual intervention by the end consumer nor any communication infrastructure nor any enhancements to the appliances or the power grids. 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. This technology could potentially be integrated into millions of future deferrable loads: appliances, electric vehicle (EV) chargers, heat pumps, water heaters, etc.

We consider the problem of scheduling jobs on a pool of machines. Each job requires multiple machines on which it executes in parallel. For each job, the input specifies release time, deadline, processing time, profit and the number of machines required. The total number of machines may be different at different points of time. A feasible solution is a subset of jobs and a schedule for them such that at any timeslot, the total number of machines required by the jobs active at the timeslot does not exceed the number of machines available at that timeslot. We present an O(log(Bmax/Bmin))-approximation algorithm, where Bmaxand Bminare the maximum and minimum available bandwidth (maximum and minimum number of machines available over all the timeslots). Our algorithm and the approximation ratio are applicable for more a general problem that we call the Varying bandwidth resource allocation problem with bag constraints (BAGVBRAP). The BAGVBRAP problem is a generalization of some previously studied scheduling and resource allocation problems.