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Residential Sector

The residential sector model aggregates data from several key industrial and governmental sources:

  • NRCan Comprehensive Energy Use Database (CEUD): Primary source for Canadian residential energy consumption, existing stock characteristics, and regional housing data.
  • EIA Annual Energy Outlook (AEO): Provides technology-specific assumptions, including capital costs, fixed O&M, and efficiencies for new and future residential technologies.
  • NREL ResStock: Utilized for high-resolution hourly energy consumption profiles (load shapes) across various housing archetypes and US states.
  • US EPA GHG Emission Factors Hub: Source for greenhouse gas emission factors (CO2, CH4, N2O) associated with various residential fuel types.
  • Renewables Ninja: Provides chronological ambient temperature and dew point data used to map US-based ResStock profiles to Canadian regional climates.

Processing and Assumptions

The model performs several transformation steps to unify these disparate data sources into a cohesive regional database:

Technology Vintaging

  • Lifetimes: Average lifetimes for residential equipment are calculated based on the mean of a Weibull distribution using parameters derived from AEO.
  • Stock Representation: Existing stock is represented through vintage-specific technologies, allowing for realistic retirement and replacement modeling.

Currency and Efficiency Conversions

  • Currency: Financial data from US sources (EIA AEO) is converted to CAD and adjusted for inflation using a GDP deflator or CPI.
  • Units: All energy data is standardized to PetaJoules (PJ) and costs to Million Dollars (M$). Efficiencies are converted to consistent dimensionless units (PJ_out / PJ_in).

Demand Specific Distributions (DSD)

  • Weather Mapping: To adapt US-state-level demand profiles (ResStock) to Canadian provinces, the model correlates hourly energy use with ambient weather conditions.
  • Mapping Logic: Hourly profiles are realigned by taking the mean of matched weather hours (temperature and humidity) from Renewables Ninja data between the source US state and target Canadian province.

Subsector Aggregation

  • The model handles five distinct subsectors independently before final merging: Space Heating, Space Cooling, Water Heating, Lighting, and Appliances.

Final Data

The output of the aggregation process is a structured SQLite database (residential.sqlite) and an optional Excel workbook. Key data structures include:

  • Commodities: Definitions for fuel inputs (natural gas, electricity, wood, etc.) and end-use demands (heating, cooling, lighting).
  • Technologies: Comprehensive set of existing (NRCan) and future (AEO) equipment with associated efficiency and cost curves.
  • DemandSpecificDistribution (DSD): Normalized hourly profiles (8760 format) for each end-use demand and region.
  • EmissionActivity: Calculated emission factors per unit of energy output, facilitating integrated climate impact analysis.