Sunil Kumar Ghai

Occupancy detection in commercial buildings using opportunistic context sources

Accurate occupancy information in commercial buildings can enable several useful applications such as energy management and dynamic seat allocation. Most prior efforts in this space depend on deploying an additional network of deeply coupled sensors to gather occupancy details. This paper presents a novel approach for occupancy detection using only context sources that are commonly available in commercial buildings such as area access badges, Wi-Fi access points, Calendar and Instant Messaging clients. We present models to conduct a situation-centric profiling using such sources and evaluate results of those models. Through a pilot study of a building floor with 5 volunteers for 6 weeks, we demonstrate the potential for detecting occupancies with accuracy as high as 90%.

User-sensitive scheduling of home appliances

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 users preferred usage profile. Using the preferred profile as a reference, Yupiks optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupiks 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.

Softgreen: Towards energy management of green office buildings with soft sensors

This paper describes an approach for saving energy in commercial buildings, based on the information gathered from pre-existing opportunistic context sources. Most energy management systems rely on a heavy instrumentation strategy to infer occupancies, and unfortunately ignore already available opportunistic context sources, that can provide significant information about occupancy. We present models to conduct a Context Profiling with available context sources, to infer spatial occupancy measures. Further, we model electrical loads of several types to infer potential energy savings. Through a pilot study of a building with 5 users for 30 days, we identify intra-building areas where additional instrumentation of occupancy sensors is not necessary and demonstrate potential for significant reduction in energy consumption. We believe such Context Profiling can provide insights to significantly reduce deployment and management costs for future occupancy detection and energy management systems.

UrJar: A Lighting Solution using Discarded Laptop Batteries

Forty percent of the worlds population, including a significant portion of the rural and urban poor sections of the population in India, does not have access to reliable electricity supply. Concurrently, there is rapid penetration of battery-operated portable computing devices such as laptops, both in the developing and developed world. This generates a significant amount of electronic waste (e-waste), especially in the form of discarded Lithium Ion batteries which power such devices. In this paper, we describe UrJar, a device which uses re-usable Lithium Ion cells from discarded laptop battery packs to power low energy DC devices. To understand the usability of UrJar in a real world scenario, we deployed it at five street-side shops in India, which did not have access to grid electricity. The participants appreciated the long duration of backup power provided by the device to meet their lighting requirements. To conclude, we present an ecosystem which consists of a community-level energy shed and UrJar devices individually owned by households, as a mechanism for DC electrification of rural areas in developing countries. We show that UrJar has the potential to channel e-waste towards the alleviation of energy poverty, thus simultaneously providing a sustainable solution for both problems.

DC picogrids: a case for local energy storage for uninterrupted power to DC appliances

An increasing number of appliances now operate on DC and providing uninterrupted power supply (UPS) to them through outages requires two conversions: first from an energy store, typically a DC battery, to AC mains and then from AC mains to the DC input required by the appliance. The energy storage and DC-to-AC inversion are usually centrally located and tied to existing AC distribution lines to amortize costs and battery capacity. In this paper, we argue that adding energy storage locally to each DC appliance and managing it intelligently can lead to higher efficiency and lower average cost. We term this topology a DC picogrid as it mimics a scaled down independent microgrid. Our contribution is the design and evaluation of a smart picogrid controller that a) identifies the power source and b) decides on battery charging or discharging based on the power source. As we expect DC picogrids to co-exist with AC UPSes, we must ensure that the DC picogrid does not draw power from the UPSs battery but charges from the macrogrid when available. To accomplish this, we exploit the fact that AC distribution from the macrogrid exhibits sufficiently distinct characteristics compared to an AC UPS or a diesel generator. Our picogrid controller uses a Hidden Markov Model for state estimation that uses temporally correlated fluctuations in line voltage and frequency for discrimination. We show through data from four settings that the controller can identify its supply source with over 90% accuracy, and that efficiency recovered from conversion losses could result in 30% reduction in energy consumption.

DC Picogrids as power backups for office buildings

Office buildings in developing countries employ battery backups with inverters and/or diesel generators to power essential loads such as lighting, air conditioning and computing loads during power cuts. Since these backup solutions are expensive and inefficient, they form a significant proportion of the operating expenses. To address this problem, we propose using a personal comfort system (an illustrative configuration can comprise a LED light and a DC desk fan) that is powered by batteries in computing devices. With this approach, cost savings are realized through two mechanisms, (i) by reducing the dependence on high-power lighting and air conditioning during times of power outage, and (ii) by charging the batteries at optimal times, taking advantage of the variable cost of power supply. Simulations show that the expected energy savings from this methodology are in the region of 26%, compared with the current system. In this paper, we present various architectures for the load-battery combination, a dynamic programming based framework that generates optimal charging/discharging schedules, and an experimental evaluation of the proposed approach.

Unlocking the hidden potential of data towards efficient buildings: Findings from a pilot study in India

Energy cost is one of the significant contributors to the operational expenses of commercial buildings. In developing countries facing problems of frequent power outages and deficient grid connectivity, diesel generators are used as backup power source which significantly increase the costs incurred in management of commercial establishments. Integration of information and communication technologies to building management systems provides a reliable platform to analyze various aspects of the building such as energy consumption trends and occupancy inferences thereby proposing reactive or pro-active strategies directed towards efficient and cost-effective building management. Usually, this potential of data available to building management agencies stays untapped in developing countries. In this paper, we take a data-driven approach to understand various operational aspects of a commercial establishment. To demonstrate the scope for optimization of building operations by exploiting the energy consumption data, a pilot study was conducted in an IT office building in India.

UrJar: A Device to Address Energy Poverty Using E-Waste

A significant portion of the population in India does not have access to reliable electricity. At the same time, is a rapid penetration of Lithium Ion battery-operated devices such as laptops, both in the developing and developed world. This generates a significant amount of electronic waste (e-waste), especially in the form of discarded Lithium Ion batteries. In this work, we present UrJar, a device which uses re-usable Lithium Ion cells from discarded laptop battery packs to power low energy DC devices. We describe the construction of the device followed by findings from field deployment studies in India. The participants appreciated the long duration of backup power provided by the device to meet their lighting requirements. Through our work, we show that UrJar has the potential to channel e-waste towards the alleviation of energy poverty, thus simultaneously providing a sustainable solution for both problems. Mode details of this work are provide in [3].