The objectives of this work are to develop a comprehensive and representative modeling framework for estimating energy consumption and associated greenhouse gas emissions in the residential sector suitable for a broad range of comparative analyses, and using this framework to develop an end-use energy consumption and greenhouse gas emissions model for the Canadian housing stock. These said objectives are successfully achieved and described in detail in this work.

A modeling framework was developed for modeling the residential energy consumption and associated greenhouse gas emissions, for cold (heating dominated) climates, at both regional and national levels. Detailed data requirements were analyzed, and existing data sources were identified and reviewed for suitability in energy modeling. Major inconsistencies and deficiencies in the existing data sources were also identified. New and emerging data sources were identified and their potential use on model development was reviewed. As a result, a comprehensive data collection campaign, including the integration of various existing and emerging data collection campaigns and sources, was proposed and the required total data collection costs were estimated for future data collections and model refinements.

A comprehensive and representative bottom-up engineering based model for estimating end-use energy consumption and associated greenhouse gas emissions for the Canadian low-rise single family residential stock was developed, and its detailed developmental procedure is fully documented in this work. This model is called the Canadian Residential End-use Energy Consumption and Emission Model (CREEEM). The model makes extensive use of current Canadian data sources to establish housing characteristics as well as to estimate the amount of energy consumption and associated GHG emissions at the regional, provincial, and national levels.

CREEEM was used to determine the energy consumption and GHG emissions from the Canadian housing stock by type of dwelling, by space heating fuel, by vintage and by province. The estimated total end-use energy consumption and GHG emissions for the 1993 low-rise single-family housing stock were 1000 PJ and 48 Mt, respectively. The average household end-use energy consumption and associated GHG emissions were estimated to be 141 GJ/year and 6.8 t/year, respectively. Electricity usage accounted for nearly half of the total energy consumption and GHG emissions in the residential sector.

The predictions of CREEEM were validated with 3248 annual energy billing records from 2811 houses. It was found that CREEEM's predictions could be used with confidence. The R2 ranged from a low of 0.81 for electricity consumption on appliance, lighting and cooling (ALC) end-uses to a high of 0.90 for natural gas consumption on combined space and domestic hot water (DHW) heating end-uses.

In addition, the prediction performance of the engineering method based CREEEM was compared with two data-driven residential energy consumption models, Neural Network (NN) and Conditional Demand Analysis (CDA), recently developed by Aydinalp (2002) using the same available data sources used to develop CREEEM. This comparison showed that all three models (CREEEM, CDA and NN), on average, have comparable overall prediction performance, and they are all capable of estimating the overall residential energy consumption, with the engineering based CREEEM being the most flexible of the three in conducting a broad range of impact analyses.

In conclusion, CREEEM, having the capability and flexibility of conducting various comparative studies and assessing policy decisions, provides the most comprehensive and representative bottom-up engineering based model for estimating end-use energy consumption and associated greenhouse gas emissions in the Canadian residential sector.