Worldwide, the residential sector is a major consumer of energy. Both the rate at which we consume energy and our use of non-renewable energy resources have come under pressure to change. These changes may occur to some extent by conservation techniques. However, due to living standard expectations, these changes will primarily rely on technology. Many technological opportunities exist to reduce the conventional energy consumption and greenhouse gas (GHG) emissions of the residential sector, such as: improving energy efficiency, introducing alternative energy conversion technologies, and increasing the use of renewable energy resources. The accurate estimate of the impact that a new technology has on residential sector energy consumption and GHG emissions requires a versatile, reliable, detailed, and high-resolution analytical model. Such models account for the wide range of climate, energy supply, and housing stock characteristics, and are useful for decision makers to evaluate and parametrically compare a wide range of energy efficiency measures and technology strategies when applied to the residential sector. This dissertation presents the development of a new energy consumption and GHG emissions model of the Canadian residential sector. This new model is detailed with regard to the housing stock, comprehensive with regard to the treatment of end-uses (including thermodynamic behaviour and occupant behaviour), and possesses the capability, resolution, and accuracy to assess the impact upon energy consumption and GHG emissions due to the application of alternative and renewable energy technologies to the residential sector. The new model is titled the Canadian Hybrid Residential End-Use Energy and GHG Emissions Model (CHREM). The CHREM advances the state-of-the-art of residential sector energy consumption and GHG emissions modeling by three principal contributions: i) a database of 16,952 unique house descriptions of thermal envelope and energy conversion system information that statistically represent the Canadian housing stock; ii) a “hybrid” modeling approach that integrates the bottom-up statistical and engineering modeling methods to account for occupant behaviour, and provide the capacity to model alternative and renewable energy technologies, such as solar energy and energy storage systems; and iii) a method for the accumulation and treatment of energy and GHG emissions results.