Data-Driven Non-Intrusive Load Monitoring for Farm Facilities based on Smart NeatMeter Devices

Canadian agriculture and agri-food system is one of the world’s strongest and plays a critical role in an economy increasingly dominated by manufacturing and service industries. Within the transition toward automation in agriculture and a significantly growing population, the tracking and visualizing farmers’ energy usage has become an unavoidable demand. Besides, the latest development in smart meter installed inside farm barns facilitates the real-time monitoring of various electrical appliances’ energy consumption and in turn help farms operate more efficiently and sustainably. Most existing load monitoring approaches so far obtain electricity data by installing sensors at appliances or power outlets. This project targets at developing a load monitor solution for farm barns based on a single meter. In this project, the Rakr Inc and the researchers at Carleton University will collaborate to develop deep learning algorithms to identify appliances’ load patterns in farm barns.

Faculty Supervisor:

Shichao Liu

Student:

Nahal Iliaee

Partner:

Rakr Inc

Discipline:

Other

Sector:

Agriculture

University:

Carleton University