ebm.model.database_manager module
- class DatabaseManager(file_handler: FileHandler = None)[source]
Bases:
objectManages database operations.
- COL_TEK = 'building_code'
- COL_TEK_BUILDING_YEAR = 'building_year'
- COL_TEK_START_YEAR = 'period_start_year'
- COL_TEK_END_YEAR = 'period_end_year'
- COL_BUILDING_CATEGORY = 'building_category'
- COL_BUILDING_CONDITION = 'building_condition'
- COL_AREA = 'area'
- COL_ENERGY_REQUIREMENT_PURPOSE = 'purpose'
- COL_ENERGY_REQUIREMENT_VALUE = 'kwh_m2'
- COL_HEATING_REDUCTION = 'reduction_share'
- DEFAULT_VALUE = 'default'
- __init__(file_handler: FileHandler = None)[source]
- get_building_code_list()[source]
Get a list of building_code.
Returns: - building_code_list (list): List of building_code.
- make_building_purpose(years: YearRange | None = None) DataFrame[source]
Returns a dataframe of all combinations building_categories, teks, original_condition, purposes and optionally years.
Parameters
years : YearRange, optional
Returns
pd.DataFrame
- get_building_codes() DataFrame[source]
Retrieve building_code_parameters
Returns
- pd.DataFrame
Pandas Dataframe containing building_code with parameters
- get_building_code_params(building_code_list: List[str] = None)[source]
Retrieve building_codeparameters for a list of building_code.
This method fetches building_codeparameters for each building_codeID in the provided list, converts the relevant data to a dictionary, and maps these values to the corresponding attributes of the TEKParameters dataclass. The resulting dataclass instances are stored in a dictionary with building_code as keys.
Parameters: - building_code_list (list of str): List of building_code.
Returns: - building_code_params (dict): Dictionary where each key is a building_codeID and each value
is a TEKParameters dataclass instance containing the parameters for that building_codeID.
- get_scurve_params()[source]
Get input dataframe with S-curve parameters/assumptions.
Returns: - scurve_params (pd.DataFrame): DataFrame with S-curve parameters.
- get_construction_population() DataFrame[source]
Get construction population DataFrame.
Returns: - construction_population (pd.DataFrame): Dataframe containing population numbers
year population household_size
- get_population_forecast_end_year() int[source]
Retrieve the maximum year from the population forecast data
This method extracts the highest value from the ‘year’ level of the MultiIndex in the construction population dataset.
Returns
- int
The maximum year available in the population forecast data.
Raises
- ValueError
If the population forecast index does not contain a ‘year’ level.
Get building category share by year as a DataFrame.
The number can be used in conjunction with number of households to calculate total number of buildings of category house and apartment block
Returns: - new_buildings_category_share (pd.DataFrame): Dataframe containing population numbers
“year”, “Andel nye småhus”, “Andel nye leiligheter”, “Areal nye småhus”, “Areal nye leiligheter”
- get_area_new_residential_buildings() Series[source]
Get population and household size DataFrame from a file.
Returns: - construction_population (pd.DataFrame): Dataframe containing population numbers
“area”,”type of building”,”2010”,”2011”
- get_building_category_floor_area(building_category: BuildingCategory) Series[source]
Get population and household size DataFrame from a file.
Returns: - construction_population (pd.DataFrame): Dataframe containing population numbers
“area”,”type of building”,”2010”,”2011”
- get_area_parameters() DataFrame[source]
Get total area (m^2) per building category and TEK.
Parameters: - building_category (str): Optional parameter that filter the returned dataframe by building_category
Returns: - area_parameters (pd.DataFrame): Dataframe containing total area (m^2) per
building category and TEK.
- get_area_start_year() Dict[BuildingCategory, Series][source]
Retrieve total floor area in the model start year for each TEK within a building category.
Returns
- dict
A dictionary where: - keys are BuildingCategory objects derived from the building category string. - values are pandas.Series with the ‘tek’ column as the index and the corresponding
‘area’ column as the values.
- get_energy_req_original_condition(year_range: YearRange | None = None) DataFrame[source]
- Get dataframe with energy requirement (kWh/m^2) for floor area in original condition. The result will be
calibrated using the dataframe from DatabaseManger.get_calibrate_heating_rv
Returns
- pd.DataFrame
Dataframe containing energy requirement (kWh/m^2) for floor area in original condition, per building category and purpose.
- get_energy_req_reduction_per_condition() DataFrame[source]
Get dataframe with shares for reducing the energy requirement of the different building conditions. This function calls explode_unique_columns to expand building_category and TEK as necessary.
Returns
- pd.DataFrame
Dataframe containing energy requirement reduction shares for the different building conditions, per building category, TEK and purpose.
- get_energy_need_yearly_improvements() DataFrame[source]
Get dataframe with yearly efficiency rates for energy need improvements. This function calls explode_unique_columns to expand building_category and TEK as necessary.
The column yearly_efficiency_improvement is expected to contain the yearly reduction as a float between 0.1 and 1.0.
Returns
- pd.DataFrame
Dataframe containing yearly efficiency rates (%) for energy need improvements, per building category, tek and purpose.
- get_energy_need_policy_improvement() DataFrame[source]
Get dataframe with total energy need improvement in a period related to a policy. This function calls explode_unique_columns to expand building_category and TEK as necessary.
Returns
- pd.DataFrame
Dataframe containing total energy need improvement (%) in a policy period, per building category, tek and purpose.
- get_area_per_person(building_category: BuildingCategory = None) Series[source]
Return area_per_person as a pd.Series
Parameters
- building_category: BuildingCategory, optional
filter for building category
Returns
- pd.Series
float values indexed by building_category, (year)