Modules¶

Main Module¶

building_energy_process.executeBuildingEnergySimulationProcess(processPayload: dict, startTime: str, endTime: str, buildingUse: str) → dict[source]¶

Function to execute the Building Energy Simulation process.

Parameters:

processPayload (dict) –
The dictionary with the process input payload:
```
Example: see "input.json" file in the root directory.
```
startTime (str) – The start datetime, e.g., “2019-03-01T13:00:00”.
endTime (str) – The end datetime, e.g., “2019-03-02T13:00:00”.
buildingUse (str) – The archetype (building use). For example: “Offices”.

Returns:

dict

building_energy_process.executeModel(modelPayload: dict) → dict[source]¶

Function to execute the Buildings Stock Energy Model.

Parameters:

modelPayload (dict) –

The dictionary with the Model input payload:

Example: see "input.json" file in the root directory.

Returns:

dict

building_energy_process.formatHourlyResults(dictHourlyResults: dict, archetypes: list) → dict[source]¶

Function to format the Hourly results.

Parameters:

dictHourlyResults (dict) –

The dictionary resulting from the Hourly results calculation. Example:

"{
    "Apartment Block": DataFrame,
    "Single family- Terraced houses": DataFrame,
    "Hotels and Restaurants": DataFrame,
    "Health": DataFrame,
    "Education": DataFrame,
    "Offices": DataFrame,
    "Trade": DataFrame,
    "Other non-residential buildings": DataFrame,
    "Sport": DataFrame
}"

archetypes (list) –

The list of building uses. Example:

"[
    "Apartment Block",
    "Single family- Terraced houses",
    "Hotels and Restaurants",
    "Health,
    "Education",
    "Offices",
    "Trade",
    "Other non-residential buildings",
    "Sport"
]"

Returns:

dict

building_energy_process.main()[source]¶

Main function.

Parameters:

sys.argv[0] (str) – The current file name, e.g., “building_energy_process.py”.
sys.argv[1] (str) – The process input data file path, e.g., “input.json”.
sys.argv[2] (str) – The start datetime, e.g., “2019-03-01T13:00:00”.
sys.argv[3] (str) – The end datetime, e.g., “2019-03-02T13:00:00”.
sys.argv[4] (str) – The archetype (building use), e.g., “Offices”.

Returns:

None

Model Module¶

building_stock_energy_model.model.s01LoadPreviousResult(nutsId: str) → tuple[source]¶

Model Step 01: Load the previous result.

Parameters:: nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
Returns:: tuple

building_stock_energy_model.model.s02RetrieveTemperatures(nutsId: str, year: int) → DataFrame[source]¶

Model Step 02: Retrieve temperatures.

Parameters:

nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
year (int) – The selected year.

Returns:

DataFrame

building_stock_energy_model.model.s03RetrieveRadiationValues(nutsId: str, year: int) → DataFrame[source]¶

Model Step 03: Retrieve radiation values.

Parameters:

nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
year (int) – The selected year.

Returns:

DataFrame

building_stock_energy_model.model.s04LoadDatabase(nutsId: str, hddReduction: float, cddReduction: float) → tuple[source]¶

Model Step 04: Load the database.

Parameters:

nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
hddReduction (float) – Reduction in heating degree days for future scenario.
cddReduction (float) – Reduction in cooling degree days for future scenarios.

Returns:

tuple

building_stock_energy_model.model.s05RetrieveSolarData(nutsId: str, year: int, listInputJsonSolar: list, dictDBBuildings: dict)[source]¶

Model Step 05: Retrieve radiation values.

Parameters:

nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
year (int) – The selected year.

listInputJsonSolar (list) –

Data from the input.json file. Example:

[
    {'building_use': 'Apartment Block', 'area_total': None, 'power': 195.826, 'capex': None},
    {'building_use': 'Single family- Terraced houses', ...},
    {'building_use': 'Offices', ...},
    {'building_use': 'Education', ...},
    {'building_use': 'Health', ...},
    {'building_use': 'Trade', ...},
    {'building_use': 'Hotels and Restaurants', ...},
    {'building_use': 'Other non-residential buildings', ...},
    {'building_use': 'Sport', ...}
]

dictDBBuildings (dict) –

Data from the database file. Example:

{
    'System': {'system_cost': 0.5, 'opex_pv': 15000, 'min_ghi_pv': 1000, 'land_use_pv': 100},
    'Apartment Block': {'tilt': 30, 'azimuth': 180, 'loss': 14, 'efficiency': 18,
        'tracking_porcentage': 0, 'use_factor': 100, 'distribution': [0.6, 0.2, 0.2]},
    'Single family- Terraced houses': {...},
    'Hotels and Restaurants': {...},
    'Health': {...},
    'Education': {...},
    'Offices': {...},
    'Sport': {.},
    'Trade': {.},
    'Other non-residential buildings': {...},
}

Returns:

DataFrame

building_stock_energy_model.model.s06AddColumnsToMainDataFrame(dfCSV: DataFrame) → DataFrame[source]¶

Model Step 06: Add columns to the main dataframe.

Parameters:: dfCSV (DataFrame) – The built input data DataFrame.
Returns:: DataFrame

building_stock_energy_model.model.s07AddInputDataToMainDataframe(dfCSV: DataFrame, dfDHW: DataFrame, dfYears: DataFrame, dfSectors: DataFrame, dfDwellings: DataFrame, nutsId: str, increaseResidentialBuiltArea: float, increaseServiceBuiltArea: float) → DataFrame[source]¶

Model Step 07: Add the input data to the main DataFrame.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfDHW – (DataFrame): The DataFrame corresponding to DHW.
dfYears (DataFrame) – The DataFrame corresponding to Years.
dfSectors (DataFrame) – The DataFrame corresponding to Sectors.
dfDwellings (DataFrame) – The DataFrame corresponding to Dwellings.
nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.
increaseResidentialBuiltArea (float) – Reduction in heating degree days for future scenario.
increaseServiceBuiltArea (float) – Reduction in cooling degree days for future scenarios.

Returns:

DataFrame

building_stock_energy_model.model.s08AddActiveMeasures(dfCSV: DataFrame, dfReshhtes: DataFrame, dfSerhhtes: DataFrame, dfRThheff: DataFrame, nutsId: str, activeMeasures: list, activeMeasuresBaseline: list, archetypes: list) → DataFrame[source]¶

Model Step 08: Add the active measures.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfReshhtes (DataFrame) – The DataFrame corresponding to Residential.
dfSerhhtes (DataFrame) – The DataFrame corresponding to Service.
dfRThheff (DataFrame) – The DataFrame corresponding to RT.
nutsId – str -> Identifier of NUTS2 region for which the analysis will be carried out.

activeMeasures (list) –

The list of active measures:

Example: see "input.json" file in the root directory.

activeMeasuresBaseline (list) –
The list of active measures corresponding to the baseline:
```
Example: see "input.json" file in the root directory.
```

archetypes (list) –

The list of building uses. Example:

"[
    "Apartment Block",
    "Single family- Terraced houses",
    "Hotels and Restaurants",
    "Health",
    "Education",
    "Offices",
    "Trade",
    "Other non-residential buildings",
    "Sport"
]"

Returns:

DataFrame

building_stock_energy_model.model.s09AddPassiveMeasures(dfCSV: DataFrame, passiveMeasures: DataFrame) → DataFrame[source]¶

Model Step 09: Add the passive measures.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.

passiveMeasures (list) –

The list of passive measures:

Example: see "input.json" file in the root directory.

Returns:

DataFrame

building_stock_energy_model.model.s10WriteUValuesAndInternalGains(dfCSV: DataFrame, dfDHW: DataFrame, dfUValues: DataFrame, dfRUValues: DataFrame, dfACH: DataFrame, nutsId: str) → DataFrame[source]¶

Model Step 10: Write the U-Values and the Internal Gains.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfDHW (DataFrame) – The DataFrame corresponding to DHW.
dfUValues (DataFrame) – The DataFrame corresponding to U-Values.
dfRUValues (DataFrame) – The DataFrame corresponding to Retroffitting U-Values.
dfACH (DataFrame) – The DataFrame corresponding to ACH.
nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.

Returns:

DataFrame

building_stock_energy_model.model.s11AddCapexDataFrame(dfCSV: DataFrame, dfBesCapex: DataFrame) → DataFrame[source]¶

Model Step 11: Add the CAPEX dataframe.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfBesCapex (DataFrame) – The DataFrame corresponding to Capex.

Returns:

DataFrame

building_stock_energy_model.model.s12AddOpexDataFrame(dfCSV: DataFrame, dfBesOpex: DataFrame) → DataFrame[source]¶

Model Step 12: Add the OPEX dataframe.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfBesOpex (DataFrame) – The DataFrame corresponding to Opex.

Returns:

DataFrame

building_stock_energy_model.model.s13AddRetrofittingCostDataFrame(dfCSV: DataFrame, dfRetroCost: DataFrame) → DataFrame[source]¶

Model Step 13: Add the Retrofitting Cost dataframe.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfRetroCost (DataFrame) – The DataFrame corresponding to Retrofitting Cost.

Returns:

DataFrame

building_stock_energy_model.model.s14AddRenewableEnergySystemsDataFrame(dfCSV: DataFrame, dfRes: DataFrame) → DataFrame[source]¶

Model Step 14: Add the Renewable Energy Systems dataframe.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfRes (DataFrame) – The DataFrame corresponding to Renewable Energy Systems.

Returns:

DataFrame

building_stock_energy_model.model.s15AddCapacityDataFrame(dfCSV: DataFrame, dfCapacity: DataFrame, archetypes: list) → DataFrame[source]¶

Model Step 15: Add the Capacity dataframe.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfCapacity (DataFrame) – The DataFrame corresponding to Capacity.

archetypes (list) –

The list of building uses. Example:

"[
    "Apartment Block",
    "Single family- Terraced houses",
    "Hotels and Restaurants",
    "Health",
    "Education",
    "Offices",
    "Trade",
    "Other non-residential buildings",
    "Sport"
]"

Returns:

DataFrame

building_stock_energy_model.model.s16AddEquivalentPowerDataFrame(dfCSV: DataFrame) → DataFrame[source]¶

Model Step 16: Add the Equivalent Power dataframe.

Parameters:: dfCSV (DataFrame) – The built input data DataFrame.
Returns:: DataFrame

building_stock_energy_model.model.s17CalculateCosts(dfCSV: DataFrame) → DataFrame[source]¶

Model Step 17: Calculate the costs.

Parameters:: dfCSV (DataFrame) – The built input data DataFrame.
Returns:: DataFrame

building_stock_energy_model.model.s18CalculateGeneralSchedule(dfCSV: DataFrame, dfSched: DataFrame, dfTemperatures: DataFrame, dfBaseTemperatures: DataFrame, dfSolarOffice: DataFrame, dfSolarNOffice: DataFrame, nutsId: str) → dict[source]¶

Model Step 18: Calculate the General Schedule for each archetype.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfSched (DataFrame) – The DataFrame corresponding to Schedule.
dfTemperatures (DataFrame) – The DataFrame corresponding to Temperatures.
dfBaseTemperatures (DataFrame) – The DataFrame corresponding to Base Temperatures.
dfSolarOffice (DataFrame) – The DataFrame corresponding to Solar Office data.
dfSolarNOffice (DataFrame) – The DataFrame corresponding to Solar Non-Office data.
nutsId (str) – Identifier of NUTS2 region for which the analysis will be carried out.

Returns:

dict

building_stock_energy_model.model.s19CalculateScenario(dfCSV: DataFrame, dictSchedule: dict) → dict[source]¶

Model Step 19: Calculate the Scenario.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.

dictSchedule (dict) –

The dictionary corresponding to the Schedule. Example:

{
    "use": "Apartment Block" "schedule": DataFrame,
    "use": "Single family- Terraced houses" "schedule" DataFrame,
    "use": "Offices", "schedule" DataFrame,
    "use": "Education" "schedule": DataFrame,
    "use": "Health", "schedule": DataFrame,
    "use": "Trade", "schedule" DataFrame,
    "use": "Hotels and Restaurants", "schedule" DataFrame,
    "use": "Other non-residential buildings" "schedule" DataFrame,
    "use": "Sport", "schedule": DataFrame
}

Returns:

dict

building_stock_energy_model.model.s20CalculateAnualResults(dfCSV: DataFrame, dictSchedule: dict) → DataFrame[source]¶

Model Step 20: Calculate the Anual Results.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.

dictSchedule (dict) –

The dictionary corresponding to the Schedule. Example:

{
    "use": "Apartment Block" "schedule": DataFrame,
    "use": "Single family- Terraced houses" "schedule" DataFrame,
    "use": "Offices", "schedule" DataFrame,
    "use": "Education" "schedule": DataFrame,
    "use": "Health", "schedule": DataFrame,
    "use": "Trade", "schedule" DataFrame,
    "use": "Hotels and Restaurants", "schedule" DataFrame,
    "use": "Other non-residential buildings" "schedule" DataFrame,
    "use": "Sport", "schedule": DataFrame
}

Returns:

DataFrame

building_stock_energy_model.model.s21CalculateConsolidate(dictSchedule: dict, archetype: str) → DataFrame[source]¶

Model Step 21: Calculate the Consolidate.

Parameters:

dictSchedule (dict) –

The dictionary corresponding to the Schedule. Example:

{
    "use": "Apartment Block" "schedule": DataFrame,
    "use": "Single family- Terraced houses" "schedule" DataFrame,
    "use": "Offices", "schedule" DataFrame,
    "use": "Education" "schedule": DataFrame,
    "use": "Health", "schedule": DataFrame,
    "use": "Trade", "schedule" DataFrame,
    "use": "Hotels and Restaurants", "schedule" DataFrame,
    "use": "Other non-residential buildings" "schedule" DataFrame,
    "use": "Sport", "schedule": DataFrame
}

archetype (str) – The name of the archetype (building use).

Returns:

DataFrame

building_stock_energy_model.model.s22CalculateHourlyResults(dfCSV: DataFrame, dfSolar: DataFrame, dictSchedule: dict, archetype: str) → DataFrame[source]¶

Model Step 22: Calculate the Hourly Results.

Parameters:

dfCSV (DataFrame) – The built input data DataFrame.
dfSolar (DataFrame) – The solar data DataFrame.

dictSchedule (dict) –

The dictionary corresponding to the Schedule. Example:

{
    "use": "Apartment Block" "schedule": DataFrame,
    "use": "Single family- Terraced houses" "schedule" DataFrame,
    "use": "Offices", "schedule" DataFrame,
    "use": "Education" "schedule": DataFrame,
    "use": "Health", "schedule": DataFrame,
    "use": "Trade", "schedule" DataFrame,
    "use": "Hotels and Restaurants", "schedule" DataFrame,
    "use": "Other non-residential buildings" "schedule" DataFrame,
    "use": "Sport", "schedule": DataFrame
}

archetype (str) – The name of the archetype (building use).

Returns:

DataFrame

building_stock_energy_model.model.x01AddFeb29IfMissingForTemperaturesData(df: DataFrame, targetYear: int) → DataFrame[source]¶

Auxiliary function 01: If the destination year is a leap year: and the original year was not, replace 28-Feb with 29-Feb.

Parameters:

df (DataFrame) – The source DataFrame.
targetYear (int) – The target year.

Returns:

DataFrame

building_stock_energy_model.model.x02AddFeb29IfMissingForRadiationData(df: DataFrame, targetYear: int) → DataFrame[source]¶

Auxiliary function 02: If the destination year is a leap year: and the original year was not, replace 28-Feb with 29-Feb.

Parameters:

df (DataFrame) – The source DataFrame.
targetYear (int) – The target year.

Returns:

DataFrame

building_stock_energy_model.model.x03DropFeb29IfPresentForTemperaturesData(df: DataFrame) → DataFrame[source]¶

Auxiliary function 03: Delete the rows for February 29 (if they exist).

Parameters:: df (DataFrame) – The source DataFrame.
Returns:: DataFrame

building_stock_energy_model.model.x04DropFeb29IfPresentForRadiationData(df: DataFrame) → DataFrame[source]¶

Auxiliary function 04: Delete the rows for February 29 (if they exist).

Parameters:: df (DataFrame) – The source DataFrame.
Returns:: DataFrame

building_stock_energy_model.model.x05RemapYear(tsIndex: DatetimeIndex, targetYear: int) → DatetimeIndex[source]¶

Auxiliary function 05: Change the year of a DatetimeIndex while keeping: the month/day/hour/minute/second values.

Parameters:

tsIndex (DatetimeIndex) – The source DatetimeIndex.
targetYear (int) – The target year.

Returns:

DatetimeIndex

building_stock_energy_model.model.x06EstablishValue(dict: dict, name: str, limitDown: float, limitUp: float, defaultValue: float) → float[source]¶

Auxiliary function 06: Establish value between limits.

Parameters:

dict (dict) – The source dictionary.
name (str) – The source property.
limitDown (float) – The lower limit of the value.
limitUp (float) – The upper limit of the value.
defaultValue (float) – The default value.

Returns:

float

building_stock_energy_model.model.x07GetAvailableArea(parameters: list, cost: float, landUse: float) → tuple[source]¶

Auxiliary function 07: Obtain the available area.

Parameters:

parameters (list) – The list of parameters.
cost (float) – The value of the cost.
landUse (float) – The value of the land use.

Returns:

tuple

building_stock_energy_model.model.x08GetRegions(dictScada: dict, area: float, minGhi: float) → tuple[source]¶

Auxiliary function 08: Obtain the regions.

Parameters:

dictScada (DataFrame) – The scada dataframe.
area (float) – The value of the area.
minGhi (float) – The value of the min GHI.

Returns:

tuple

building_stock_energy_model.model.x09GetPVProduction(rows: list, landUse: float, tilt: float, distribAzimuth: float, tracking: float, loss: float, porcent: float, year: int) → list[source]¶

Auxiliary function 09: Obtain the PV production.

Parameters:

rows (list) – The list of rows.
landUse (float) – The value of the land use.
tilt (float) – The value of the tilt.
distribAzimuth (float) – The value of the distribAzimuth.
tracking (float) – The value of the tracking.
loss (float) – The value of the loss.
porcent (float) – The value of the porcent.
year (int) – The year.

Returns:

list

building_stock_energy_model.model.x10GetDistribution(rows: list, label: str) → tuple[source]¶

Auxiliary function 10: Obtain the distribution.

Parameters:

rows (list) – The list of rows.
label (str) – The label.

Returns:

tuple

building_stock_energy_model.model.x11GetCoordinates(sourceDF: DataFrame) → tuple[source]¶

Auxiliary function 11: Obtain the coordinates.

Parameters:: sourceDF (DataFrame) – The source DataFrame.
Returns:: tuple

building_stock_energy_model.model.x12CalculateOPEX(pot: dict, opexPV: float) → tuple[source]¶

Auxiliary function 12: Calculate the OPEX.

Parameters:

pot (dict) – The pot dictionary.
opexPV (float) – The OPEX for PV.

Returns:

tuple

Validator Module¶

building_stock_energy_model.validator.validateCommandLineParameters(parameters: list)[source]¶

Funtion to validate the command line parameters.

Parameters:

parameters (list) –

The list of command line parameters. Example:

[
    "building_energy_process.py",
    "input.json",
    "2019-03-01T13:00:00",
    "2019-03-02T13:00:00",
    "Offices"
]

Returns:

None

building_stock_energy_model.validator.validateDatabaseIntegrity()[source]¶

Funtion to validate the integrity of the database.

Parameters:: None
Returns:: None

building_stock_energy_model.validator.validateLimits(value: float, limitDown: float, limitUp: float) → bool[source]¶

Function to check the limits of a given value.

Parameters:

value (float) – The value of the property to be evaluated.
limitDown (float) – The lower limit.
limitUp (float) – The highest limit.

Returns:

bool

building_stock_energy_model.validator.validateProcessOutput(result: dict) → bool[source]¶

Funtion to validate the process output.

Parameters:

result (dict) –

The process result. Example:

{
     "Apartment Block": DataFrame,
     "Single family- Terraced houses": DataFrame,
     "Offices",: DataFrame,
     "Education": DataFrame,
     "Health": DataFrame,
     "Trade": DataFrame,
     "Hotels and Restaurants": DataFrame,
     "Other non-residential buildings": DataFrame,
     "Sport": DataFrame
 }

Returns:

bool

building_stock_energy_model.validator.validateProcessPayload(payload: dict) → dict[source]¶

Funtion to validate the process payload.

Parameters:

payload (dict) –

The process payload:

Example: see "input.json" file in the root directory.

Returns:

dict