A New Hybrid End-use Energy and Emmissions Model of the Canadian Housing Stock

The structure and development of a new state-of-the-art hybrid energy end-use and greenhouse gas (GHG) emissions model of the Canadian housing stock (CHS), with the capability of accurately determining the effects of renewable energy technologies, is presented. The model incorporates a 17,000 house database developed using the latest data available from the EnerGuide for Houses database, Statistics Canada housing surveys, and other available housing databases. The geometry and thermal envelope of each house is defined in detail, making it suitable for energy simulation based on engineering methods (thermodynamics and heat transfer). The model utilizes the ESP-r building energy simulation program as its simulation engine.

To account for effects due to occupant behaviour, a new neural network methodology is utilized to estimate the demographic dependencies of the annual energy consumption of discretionary end-uses such as appliances, lighting and domestic hot water. A new approach is used to allot the annual energy consumption of discretionary end-uses to hourly or sub-hourly intervals based on occupancy, appliance, lighting and domestic hot water load profiles. Each house’s profiles are incorporated into the model as loads, a portion of which becomes internal heat gain for inclusion in the space heating/cooling simulation conducted using ESP-r.

A new method is used to calculate the GHG emissions from electricity consumption used in the residential sector based on the actual electrical generation fuel mix and the marginal fuel used in each province as a function of the time of year.

The developing hybrid model will predict energy consumption and GHG emissions of the CHS based on the latest available stock databases and surveys and uses multiple simulation methods to account for envelope losses and occupant behaviour. When complete, the model may be used to assess the impacts of envelope upgrades or the addition of renewable energy technologies based on high resolution, targeted application.