Residential Efficiency Component

class aaem.components.residential_buildings.component.ResidentialBuildings(community_data, forecast, diag=None, prerequisites={})

Bases: aaem.components.annual_savings.annual_savings.AnnualSavings

Residential energy efficiency component of the Alaska Affordable Energy Model: This module estimates the potential improvements to heating efficiency of residential buildings (homes). Consumption and savings are based on the number of units that have not been retrofit as of 2010, the performance improvements as a percentage of the pre-retrofit consumption, and the forecasted price of offset heating fuels. The cost to retrofit each home is also calculated.

Parameters:

community_data : CommunityData

CommunityData Object for a community

forecast : Forecast

forecast for a community

diagnostics : diagnostics, optional

diagnostics for tracking error/warning messages

prerequisites : dictionary of components, optional

prerequisite component data this component has no prerequisites leave empty

See also

aaem.community_data

community data module, see information on CommunityData Object

aaem.forecast

forecast module, see information on Forecast Object

aaem.diagnostics

diagnostics module, see information on Diagnostics Object

Attributes

diagnostics	(diagnostics) for tracking error/warning messages initial value: diag or new diagnostics object
forecast	(forecast) community forecast for estimating future values initial value: forecast
cd	(dictionary) general data for a community. Initial value: ‘community’ section of community_data
comp_specs	(dictionary) component specific data for a community. Initial value: ‘Residential Buildings’ section of community_data

Methods

calc_annual_electric_savings()

calculate annual electric savings created by the project

Attributes

annual_electric_savings	(np.array) electric savings ($/year) are the difference in the base
and proposed fuel costs

calc_annual_heating_savings()

calculate annual heating savings created by the project

Attributes

annual_heating_savings

(np.array) heating savings ($/year)

calc_avg_consumption()

Get the average monthly consumption of electricity for a house.

Attributes

avg_kWh_consumption_per_HH

(float) average electric consumption per household (kWh/year). >= 6000

calc_baseline_fuel_consumption()

Calculate baseline fuel consumption

Attributes: baseline_fuel_Hoil_consumption : np.array

baseline heating fuel consumption

baseline_fuel_wood_consumption

: np.array

baseline cordwood consumption

baseline_fuel_gas_consumption

: np.array

baseline natural gas consumption

baseline_fuel_LP_consumption

: np.array

baseline propane consumption

baseline_fuel_kWh_consumption

: np.array

baseline electricity consumption

baseline_HF_consumption

: np.array

baseline total heating fuel consumption

calc_baseline_fuel_cost()

calculate base line heating fuel costs

Attributes

baseline_HF_cost

(np.array) baseline cost of heating fuels per year

calc_baseline_kWh_consumption()

Calculate the baseline kWh consumption for a community

Attributes

baseline_kWh_consumption

(np.array) electric consumption per yer

calc_baseline_kWh_cost()

calculate baseline electricity costs

Attributes

baseline_kWh_cost

(np.array) baseline cost of electricity per year

calc_capital_costs()

Calculate the capital costs.

Attributes

capital_costs

(float) total cost of improvements ($)

calc_consumption_by_fuel(fuel_amnt, total_consumption, HH, cf)

calculate consumption by fuel from the total consumption

Parameters:

fuel_amnt: float

% of fuel used

total_consumption : float

total consumption for residential buildings

HH : float

a # of houses

cf: float

conversion factor

Returns:

float:

fuel consumed for a type of fuel

calc_init_HH()

Estimate the # of households for the first year of the project

Attributes

init_HH

(int) estimated households for first year of project

calc_init_consumption()

Calculate the initial consumption for each fuel type.

Attributes

init_HF	(float) initial heating oil consumption
init_wood	(float) initial heating cordwood consumption
init_gas	(float) initial natural gas fuel consumption
init_LP	(float) initial propane consumption
init_kWh	(float) initial electric consumption

calc_proposed_fuel_consumption()

Calculate the proposed heating fuel consumption

Attributes

proposed_fuel_Hoil_consumption	(np.array) proposed heating oil consumption
proposed_fuel_wood_consumption	(np.array) proposed cordwood consumption
proposed_fuel_LP_consumption	(np.array) proposed LP consumption
proposed_fuel_gas_consumption	(np.array) proposed natural gas consumption
proposed_fuel_kWh_consumption	(np.array) proposed electric consumption
proposed_HF_consumption	(np.array) proposed total electric consumption

calc_proposed_fuel_cost()

Calculate proposed heating cost

Attributes

proposed_HF_cost

(np.array) proposed heating fuel cost

calc_proposed_kWh_consumption()

calculate the proposed kWh consumption for a community

Attributes

proposed_kWh_consumption

(np.array) set to baseline values

calc_proposed_kWh_cost()

Calculate post retrofit electricity costs

Attributes

proposed_kWh_cost: np.array

proposed electricity cost

calc_savings_opportunities()

Calculate savings opportunities

Attributes

opportunity_HH	(int) Houses that can be retrofit
savings_HF	(float) savings in heating oil consumption
savings_wood	(float) savings in heating cordwood consumption
savings_gas	(float) savings in natural gas fuel consumption
savings_LP	(float) savings in propane consumption
savings_kWh	(float) savings in electric consumption
savings_mmbtu: float	total savings in mmbtu

get_fuel_total_saved()

Get total fuel saved.

Returns:

float

the total fuel saved in gallons

get_total_energy_produced()

Get total energy produced.

Returns:

float

the total energy produced

run(scalers={'capital costs': 1.0})

Runs the component. The Annual Total Savings,Annual Costs, Annual Net Benefit, NPV Benefits, NPV Costs, NPV Net Benefits, Benefit-Cost Ratio, Levelized Cost of Energy, and Internal Rate of Return will all be calculated. There must be a known Heat Recovery project for this component to run.

Parameters:

scalers : dictionary of valid scalers, optional

Scalers to adjust normal run variables. See note on accepted scalers

Notes

Accepted scalers: capital costs.

Attributes

run	(bool) True in the component runs to completion, False otherwise
reason	(string) lists reason for failure if run == False

save_component_csv(directory)

Save the component output csv in directory

Parameters:

directory : path

output directory

set_forecast_columns()

Set columns in the the forecast to values calculated in this component

Residential Efficiency configuration

Contains configuration info for community data yaml file, and

other set-up requirements

Residential Efficiency inputs

input functions for Residential Efficiency component

aaem.components.residential_buildings.inputs.process_data_import(data_dir)

Load building inventory data from residential_data.csv

Parameters:

data_dir: path

path to data directory for community

Returns:

DataFrame

residential data

Residential Efficiency outputs

output functions for Residential Efficiency component

aaem.components.residential_buildings.outputs.communities_summary(coms, res_dir)

Saves the summary by: community residential_building_summary.csv

Parameters:

coms : dictionary

results from the model, dictionary with each community or project as key

res_dir : path

location to save file

aaem.components.residential_buildings.outputs.component_summary(results, res_dir)

Creates the regional and communities summary for the component in provided directory

Parameters:

results : dictionary

results from the model, dictionary with each community or project as key

res_dir : path

location to save file

aaem.components.residential_buildings.outputs.create_regional_summary(results)

Creates the regional summary

Parameters:

results : dictionary

results from the model, dictionary with each community or project as key

Returns:

DataFrame

containing regional results

aaem.components.residential_buildings.outputs.save_regional_summary(summary, res_dir)

Saves the summary by region

Parameters:

summary : DataFrame

compiled regional results

res_dir : path

location to save file

Residential Efficiency preprocessing

preprocessing functions for Residential Efficiency component

aaem.components.residential_buildings.preprocessing.preprocess(preprocessor, **kwargs)

Preprocess Residential data

Parameters:

preprocessor: aaem.preprocessor.Preprocessor object

Returns:

dict

data for component configuration