Non--Intrusive Load Monitoring

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This proposed research program is for the invention of new algorithms, models, and systems to solve the difficult challenge of disaggregating total power readings. Disaggregating power/energy data is known as “non-intrusive load monitoring” (NILM). The goal is to use the power utility's smart meter for a house, building, microgrid, etc., to infer and track the performance of appliances and loads without the aid of sub-meters. This will help create smart, energy-efficient environments. While disaggregation is a difficult, ill-posed problem, with advanced algorithms and good statistical models, disaggregation can become feasible.Disaggregation can help reduce energy consumption to create a sustainable future by meeting our Paris Climate Agreement commitment to cut carbon emissions 30% below 2005 levels by 2030. Power consumption by residential homes and commercial buildings continues to increase each year despite various government incentives and more energy-efficient appliances on the market. The most recent Statistics Canada data show that Canadian households have increased their energy consumption by 4.5% in 2013 (97.5 GJ/household) from 2011 (93.3 GJ/household). User studies suggest that simply showing appliance data to occupants in real-time can reduce energy consumption by 14%.This program investigates the theory and limits of disaggregation by inventing and deploying highly advanced unsupervised learning algorithms and rigorous statistical models that require little or no prior data. Knowledge visualization theory is also explored to increase the occupants' understanding of how loads consume energy. Augmented reality will be explored as a way to convey consumption information. New Big Data algorithms will be needed to augment energy/power data with other available sensor data in real-time, which can potentially improve NILM accuracy. This proposed program also looks at developing standardized evaluation metrics that measure the accuracy of algorithms and models, as well as open-source tools such as emulators and simulators that can further help in the reproduction and comparison of algorithms developed by the research community at large.A program such as this requires researchers to be interdisciplinary to understand sustainability. Researchers trained in this program will gain exposure/expertise in environmental awareness, smart grid and power grid, visual analytics, software engineering, machine learning, sensors and automation, and data collection. Understanding sustainability is becoming an essential skill for the 21st-century economy, and this program will help contribute to creating the leaders and innovators of tomorrow. Additionally, disaggregation will provide solutions to meet Canada's international commitment to the Paris Climate Agreement by reducing the impacts of climate change.