The Ontario electrical grid is sized to meet peak electricity load. A reduction in peak load would allow deferring large infrastructural costs of additional power plants, thereby lowering generation cost and electricity prices. Proposed solutions for peak load reduction include demand response and storage. Both these solutions require accurate prediction of a home's peak and mean load. Existing work has focused only on mean load prediction. We find that these methods exhibit high error when predicting peak load. Moreover, a home's historic peak load and occupancy is a better predictor of peak load than observable physical characteristics such as temperature and season. We explore the use of Seasonal Auto Regressive Moving Average (SARMA) for peak load prediction and find that it has 30% lower root mean square error than best known prior methods.

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With the advent of utility-owned smart meters and smart appliances, the amount of data generated and collected about consumer energy consumption has rapidly increased. Energy usage data is of immense practical use for consumers for audits, analytics, and automation. Currently, utility companies collect, use, share, and discard usage data at their discretion, with no input from consumers. In many cases, consumers do not even have access to their own data. Moreover, consumers do not have the ability to extract actionable intelligence from their usage data using analytic algorithms of their own choosing: at best they are limited to the analysis chosen for them by their utility. We address these issues by designing and implementing a cloud-based architecture that provides consumers with fast access and fine-grained control over their usage data, as well as the ability to analyse this data with algorithms of their choosing, including third party applications that analyse that data in a privacy preserving fashion. We explain why a cloud-based solution is required, describe our prototype implementation, and report on some example applications we have implemented that demonstrate personal data ownership, control, and analytics.

Researchers who develop new home technologies using connected devices often want to conduct large-scale field studies in homes to evaluate their technology, but conducting such studies today is extremely challenging. Inspired by the success of PlanetLab, which enabled development and evaluation of global network services, we are developing a shared infrastructure for home environments, called Lab of Things. Our goal is to substantially lower the barrier to developing and evaluating new technologies for the home environment.

Networked sensors and actuators are increasingly permeating our computing devices, and provide a variety of functions for Internet of Things (IoT) devices and applications. However, this sensor data can also be used by applications to extract private information about users. Applications and users are thus in a tussle over access to private data. Tussles occur in operating systems when stakeholders with competing interests try to access shared resources such as sensor data, CPU time, or network bandwidth. Unfortunately, existing operating systems lack a principled approach for identifying, tracking, and resolving such tussles. Moreover, users typically have little control over how tussles are resolved. Controls for sensor data tussles, for example, often fail to address trade-offs between functionality and privacy. Therefore, we propose a framework to explicitly recognize and manage tussles. Using sensor data as an example resource, we investigate the design of mechanisms for detecting and resolving privacy tussles in a cyber-physical system, enabling privacy and functionality to be negotiated between users and applications. In doing so, we identify shortcomings of existing research and present directions for future work.