• Smart Homes and Buildings

    Non-cash incentives

    The goal of our work is to study the role of non-cash incentives in motivating behavioural change. We are motivated by the fact that the rapid adoption of smartphones has made it possible for home users to gain unprecedented access to their home energy usage. Moreover, it is already possible for a smartphone to display home and building energy usage status and trends, and to turn appliances and other equipment on and off .

    In this project, we wish to take this work several steps further. Specifically, we want to study what incentivizes a home or building owner to make this behavioural change? Our hypothesis is that the use of social media and smartphones makes it possible to dramatically improve the role of non-cash incentives. Specifically, we would like to use game theory to study the use of peer pressure and competition to drive down energy usage, similar to the use of peer pressure to enforce good behaviour in peer-to-peer networks. We will build social network applications on smartphones that not only give a home or building owner hints on how to reduce energy usage but also incentivize this behaviour.The key focus of our work will be to design a flexible architecture to allow us to experiment with a variety of schemes for non-cash incentives, implementing the appropriate communication and control protocols as necessary.

    Cloud-oriented architecture for smart home data management

    Smart grid initiatives look to generate massive amounts of data concerning consumers’ energy consumption, which although useful is privacy sensitive in nature. This project develops a system to allow third party application development targeted towards this data while retaining its privacy, ownership and control.

    Energy-optimal routing for smart home sensor networks

    In the future, we expect that day to day objects will be enhanced with computation and communication to make them into smart objects. Many smart object networks however, use unreliable communication media such as low-power wireless communication and power line communication, where the communication takes place over the wires in the electrical grid. They also operate in a highly constrained environment in terms of physical size, available memory, CPU power and battery life. The unreliable link characteristics and scarce resources have motivated the design of routing protocols for smart object networks such as the RPL (Routing Protocol for Low-power and Lossy Networks) protocol. RPL is the IETF (lnternet Engineering Task Force) standard IPv6 routing protocol for smart object networks. Although RPL was deemed ready in 2011, it has several undefined specifications which greatly impact its performance. In this work, we have come up with energy-aware routing metrics as well as transmission power control algorithms that decrease the energy cost of RPL.

    Home peak load prediction

    Proposed smart grid solutions to reducing peak electricity demand include storage and demand response  which require a dynamic prediction of electricity demand. This project involves design of prediction models to predict peak electricity demand of a home on a relatively short timescale such as an hour.

    • R. P. Singh, P. X. Gao, D. J. Lizotte. On Hourly Peak Load Prediction, Proc. IEEE SmartGridComm,  November 2012. Winner of Best Paper Award in its track.

    Smart appliances

    In our previous study we have observed significant gains due to elasticity of appliances. In this project, we extend the notion of appliance elasticity by proposing a scheme for appliances to respond to signals received from the power grid and a scheme for the utility to compute the signals to send to appliances so that the carbon footprint of the power grid, caused by commissioning plants with high carbon emissions, will be reduced.

    Temperature setpoint market

    The electrical grid is designed to meet peak loads, which may occur for only a few hours each year. Consequently, there are significant economic gains from a reduction in the peak load. Air conditioner (AC) load from residential buildings forms a significant portion of peak summer loads. The existing ‘peaksaver’ program in Ontario attempts to reduce AC loads by setting thermostats a few degrees higher in volunteer households on hot summer days. This has had only a limited success. To address this issue, we propose a scheme that provides monetary incentive for participation. We describe the operation of this ‘temperature market’ and demonstrate its effectiveness with a heterogeneous population of potential participants.

    Real time data monitoring and anomaly of solar panels

    In this project, we monitored, in real time, individual solar panels on the roof of the Environment 3 (EV3) building at the University of Waterloo. This project also comprised design and implementation of hardware and software for detecting the anomalies on solar panels, such as shadows, snow, hail, dust and clouds. A sensor network to measure solar panel outputs was built, using the Zigbee protocol. We deployed the system on roof-top solar panels and developed machine learning techniques for automated analysis and detection of anomalies in current and voltage outputs.

    The effectiveness of pricing on residential electrical storage for the smart grid

    Storage batteries that can store energy during times of low load and be discharged during times of peak load, is one proposed solution to reduce peak load. This project evaluates the effect of storage on the electrical grid under different consumer electricity pricing schemes.

    Elasticity of Domestic Appliances

    Power demand from the residential sector represents a significant fraction of overall demand. Most schemes attempting to curb residential demand rely upon human reactions to pricing signals. We have discovered an inherent property called elasticity present in most appliances that will allow elastic components in appliances to instantaneously reduce their power consumption without any impact on their lifetime. When these components reduce their power consumption, the time required to complete their operation will increase in proportion. The more the time is extended, the more is the degree of possible reduction of comfort for consumers. We had conducted a preliminary assessment of the peak demand reduction potential for regions with different demographics with various degrees of comfort reduction. This study had indicated significant gains in peak demand reduction even for a minor degree of comfort reduction.

    Building monitoring

    We deployed a testbed of 24 wired sensors in our building and  gathered data in sound and light levels, temperature, and wind velocity. We  used Partially-observable Markov decision processes to optimally control heating vents to turn off heating and cooling when users are inactive, and to turn it back on when we can predict impending activity based on past patterns of activity.

    Smartphone based energy consumption feedback

    This project investigated the effect of native smartphone applications on consumers’ energy aware behavior, in light of the now defunct, browser-based commercial services like Google Powermeter and Microsoft Hohm.

    Interactive home monitoring and control

    Our vision of the future home is of an autonomous power generation system that is capable of modulating its consumption in response to system signals as well as augmenting its energy consumption with renewable resources. This includes the real-time monitoring and control of renewable power generation, power storage, and measurement devices. Designed and implemented a fully interactive energy monitoring and control system for a home, comprising off-the-shelf hardware, existing home appliances, Android smartphone applications, intelligent e-mail bots, and a home energy gateway implemented using Microsoft Home OS. Watch this demo for an abnormal consumption e-mail alert application.