Degradation assessment for critical assets in power generation has great significance for power industry. Existing degradation assessment models failed either in combining the multimodal condition monitoring data or in removing time-varying working condition influences, resulting in inaccurate and/or unreliable degradation assessment results. In order to achieve robust and accurate degradation assessment for power generation critical assets, this project aims at developing new models based on both maintenance data and condition monitoring data from ENMAX.
Heating and cooling in residential and commercial buildings account for 20% of total energy consumption in Canada. Conditioning indoor air using less energy is closely associated with minimizing production of greenhouse gases and making a sustainable global environment. In this study, we apply a nanocomposite fibrous membrane for an energy recovery ventilator (ERV). Heat and moisture from an exhaust contaminated indoor air are captured via ERV and recycled for conditioning entering outdoor air, ultimately resulting in energy savings and improving the indoor air quality of buildings.
The aim of this project is to predict building energy consumption for next days. Firstly, we will collect several data such as weather data, time data, and historical energy consumption of the building. We will analyze the collected data to recognize the usage patterns of the building, for instance, the low and the high electricity consumption of building. After data analysis, we will apply several machine learning models to predict energy consumption of building, and finally all models will be compared to choose the outperform method.
This research will investigate Wide Area Measurement based controllers for improving stability in systems with HVDC and FACTS devices embedded in AC networks. The approach will extend the candidates Ph.D. research which introduced a new method that is always able to guarantee improved damping of all modes in the face wide changes in the network. The approach will lead to controller designs which are robust against configuration or operating point changes, or communication loss.
AYO Smart Home is an integrator of new technologies to provide affordable and energy-efficient housing for First Nations communities across Canada. AYO manages the technology and supply chain to deliver Net-Zero houses consisting of efficient building envelopes, heat recovery systems, energy-efficient HVAC, LED lighting, mold-resistant materials, and smart home controllers.
Canada is becoming an international leader in energy storage systems. Battery energy storage systems (BESS) are one of the key parts of the storage landscape and can serve a wide range of applications across the electricity supply chain. The focus of this research is on the application of BESS in mitigating power quality issues in industrial facilities.
West 5 community in London, Ontario, will pursue high penetration of electric vehicles, and is exploring an innovative marketing program of including them with the sale of each new condominium unit. The primary objective of this project is to determine an economical approach to create an acceptable infrastructure for these electric vehicles that will be desired by the community. The study will evaluate how to improve the efficiency of using Solar Energy to charge vehicles battery and for other DC loads in the London West 5 community.
To research, design, and develop a network communication and control modules that integrate any residential HAVC control system with a utility energy management user interface. Developed signal modulation scheme will be implemented on development testing board. Device will network with all utilities for gas, water, and electricity.
It has been noted in recent studies that provided an increase in the lipid content of the field pea (Pisum Sativum L.) through genetic manipulation, it can be used as a viable commercial alternative to conventional oilseed crops, which include canola and soybean. Genetic transformants with high lipid content can be created in the McGill University laboratories but its commercial viability needs to be tested with an industry partner.
Accurate measurements of mass flow rate in a pipe is crucial to virtually every industrial process where a fluid is moved from place to place. The velocity measured in a pipe is often determined by measuring the pressure drop over an orifice plate. Once this orifice plate is properly calibrated, the velocity and in turn, the mass flow through the pipe can be calculated. A downside of the orifice plates is that the plates need to be calibrated. Proper calibration of the orifice plate is essential so that that mass flow rate can be accurately predicted using the pressure drop measurements.