Energy and Water Disaggregation for Non-Intrusive Load Monitoring in Buildings

HOME | RESEARCH AREAS

ABOUT THE PROJECT

The research proposes to improve sustainability in water and energy through improved non-intrusive load monitoring (NILM). Specifically, the project aims to improve disaggregation techniques and propose a new area of cross domain disaggregation.

RESEARCH ABSTRACT

Given the limited resources of energy and water, global efforts in sustainability aim to reduce wastage and improve utilization. In recent times, one of the key elements to improve sustainability in water and energy has been the topic of non-intrusive load monitoring (NILM). The goal of NILM is to identify the energy and water consumption at the appliance level, so that wastage can be identified and hence prevented. The de facto technique for NILM is signal disaggregation – it aims at segregating the water/energy consumption of individual appliances given an aggregate reading from the smart-meter. This is a highly challenging problem since there are multiple appliances attached to a single smart-meter.

We propose to tackle several problems in this area:

  1. We will improve disaggregation techniques by leveraging ideas from deep learning.
  2. We propose a new area – cross domain disaggregation. Many a times, only the aggregate smart-meter data is available without any information regarding the appliances attached to it, which raises the question – how do we disaggregate?
  3. We propose a less intrusive and less expensive training phase for disaggregation problems. Currently, in the training phase, the data from every appliance need to be logged by its individual smart-meter. This is highly intrusive and expensive for large buildings. We propose to learn disaggregation from the aggregate smart-meter reading and state information of the device only.
  4. We will create a comprehensive set of standards and guidelines that documentation best practices used for the disaggregation industry and for testing disaggregation hardware/products.

Project Team

Dr. Ivan Bajic,  Simon Fraser University
Dr. Angshul Majumdar, IIT Delhi

Partners

Simon Fraser University
Indraprastha Institute of Information Technology, Delhi
Zenatix

Research Projects

IC-IMPACTS funded research is driven by a commitment to research excellence and supports the discovery and application of solutions to some of the most pressing issues in both Canadian and Indian communities.

GBM-CLIMPACT: Development of an end-to-end modeling and analysis toolset to assess climate impact and readiness of water sector in the Ganga, Brahmaputra, and Meghna basins

Canadian PI: Dr. Martyn Clark                                   Canadian Institution: University of Saskacthewan Indian PI: Dr. Manabendra Saharia…

Use of deep learning models to understand the impact of climate and land use changes on future groundwater resources, with a focus on data scarce regions.

Canadian PI: Dr. Jan Franklin Adamowski                                             Canadian Institution: Mcgill University Indian PI: Dr. R.Maheswaran Rathinasamy…

Machine learning methods for water quality estimation and control in water resource recovery facilities: Towards Circular Economy and Sustainability

Canadian PI: Dr. Peter Vanrolleghem                      Canadian Institution: Université Laval Indian PI: Dr. Seshagiri Rao Ambati…

FOLLOW US

Ornare quam viverra orci sagittis eu volutpat odio facilisis.

Latest news & article.

Ornare quam viverra orci sagittis eu volutpat odio facilisis.