How we use energy reveals vital information about our everyday behaviour – from our work and school schedules to when we are likely to be sleeping, eating and performing simple chores such as laundry or washing dishes. It even reveals when we are on vacation. While this information in the wrong hands could put us at risk, it also plays an important role in reducing our carbon footprint and ensuring the cost of energy is kept relatively low. Power generation companies use information derived from microclimate, small areas within general climate zones, to accurately predict power generation needs. Microclimate information is captured from smart meters, which wirelessly transmits data from our homes to utility companies. Considering that more than one million smart meters have been installed in Ontario homes since 2010 and that number continues to grow, it’s necessary to find a way to ensure the data captured by smart meters is protected and secure from threat.

This projects aims using SOSCIP’s computing platforms to help power generation companies capture the data they need to accurately make these predictions while also respecting consumers’ privacy. Using the SOSCIP Cloud Data Analytics Platform the project team will test the formula’s validity using simulated data. The project is also aim to develop an algorithm which will allow to predict electricity usage in near-real time by a third party while keeping the data captured by smart meters secure. Essentially, the secure linear regression algorithm allows researchers to compute numbers without actually being able to view those numbers.

Industry Partner(s):IBM Canada Ltd.

Academic Institution:Ryerson University

Academic Researcher: Andriy Miranskyy

Co-PI Name: Ayse Bener, Ali Miri & Matt Davison

Focus Areas: Energy

Platforms: Cloud