Residential Sector Data Collection

Overview and Assumptions: A. Data on the number and average size of occupied housing units from the biennial American Housing Survey were employed to generate many of the activity metrics for this sector. B. Three types of residential housing units are distinguished: single-family, multi-family, and manufactured homes. C. Regional data from EIA’s State Energy Data System (SEDS) are employed to develop regional intensity indicators. D. Regression models at the regional level are used to adjust for year-to-year changes in weather. E. Two separate data construction elements are required to generate the regional and national estimates of energy intensity indicators for this sector.

  1. Regional time series of floor space for residential housing units in the U.S (census level).

  2. Weather adjustment for the four census regions.

class EnergyIntensityIndicators.residential.ResidentialIndicators(directory, output_directory, level_of_aggregation=None, lmdi_model='multiplicative', base_year=1985, end_year=2018)

Bases: EnergyIntensityIndicators.LMDI.CalculateLMDI

activity(floorspace)

Combine Energy datasets into one Energy Consumption Occupied Housing Units

collect_data()

Gather all input data for you in decomposition of energy use for the Residential sector

collect_weather(energy_dict, nominal_energy_intensity)

Collect weather data for the Residential Sector

fuel_electricity_consumption(total_fuels, elec, region)

Combine Energy datasets into one Energy Consumption dataframe in Trillion Btu Data Source: EIA’s State Energy Data System (SEDS)

get_floorspace()

Collect floorspace data for the Residential sector

get_seds()

Collect SEDS data

main(breakout, calculate_lmdi)

Calculate decomposition for the Residential sector