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pull from: CERC:main
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tests_unit ... main

Author SHA1 Message Date
jgavalda
04a56b5695 Rechanged the Minimal.idf to give the necessary results, without additional stuff not used by the tool 2024-01-29 11:46:06 -05:00
jgavalda
082c44b38d Tests to incorporate HVAC systems to EP workflow 2023-11-16 15:51:58 -05:00
p_monsalvete
428b31354e adapted to no dataframes results 2023-08-11 15:44:25 -04:00
p_monsalvete
5cb01fc74c Merge remote-tracking branch 'origin/main' into main 
# Conflicts:
#	main.py
 2023-07-11 16:36:16 -04:00
p_monsalvete
a2b2ba890c updated sra to the latest version and erased unneeded lines in main 2023-07-11 16:35:13 -04:00
jgavalda
53b2227589 Ongoing developments for the course workshop, including graphs implementation 2023-07-07 10:12:48 -04:00
p_monsalvete
1a0ec10311 added case study 2023-06-13 15:51:59 -04:00
p_monsalvete
4f88ddef49 Merge remote-tracking branch 'origin/main' into main 2023-06-08 10:40:00 -04:00
jgavalda
d65e372500 Test 2023-06-08 10:38:53 -04:00
jgavalda
8f533114c6 Merge remote-tracking branch 'origin/main' 2023-06-07 17:02:01 -04:00
p_monsalvete
537809f3c8 adjusting to changes in hub 2023-06-07 14:48:21 -04:00
p_monsalvete
9f8642d0b5 Merge remote-tracking branch 'origin/main' into main 2023-06-01 14:12:30 -04:00
p_monsalvete
89c7f07d00 erased sizing part 2023-06-01 14:12:15 -04:00
jgavalda
b76223a2d6 Credit option incorporated and working correctly 2023-06-01 08:41:09 -04:00
Guille
a566d38a85 Complete basic clean up and warning removal 2023-05-31 12:20:02 -04:00
Guille
9ff47087eb Complete basic clean up and warning removal 2023-05-31 10:49:17 -04:00
jgavalda
628f43ee78 Deleted multiplication by 12 in peak 2023-05-30 08:14:11 -04:00
p_monsalvete
8dd6d4c235 working project with all results 2023-05-29 15:10:15 -04:00
p_monsalvete
f9f31ac154 Merge remote-tracking branch 'origin/main' into main 2023-05-29 14:59:03 -04:00
p_monsalvete
1f2bd09ac7 added more results to the list 2023-05-29 14:58:51 -04:00
jgavalda
1f9ef0513a Merge remote-tracking branch 'origin/main' 2023-05-19 16:12:39 -04:00
p_monsalvete
1cb4050ae4 updating readme 2023-05-19 15:49:57 -04:00
jgavalda
ba2d117d08 Incorporation of ventilation. Working. 2023-05-19 11:20:42 -04:00
p_monsalvete
c04493b8a1 cleaned formatting 2023-05-18 11:36:45 -04:00
p_monsalvete
8ec092a1ae added peak loads and consumption to results of the workflow 2023-05-18 11:33:14 -04:00
p_monsalvete
229112387e Merge remote-tracking branch 'origin/main' into main 2023-05-02 12:42:42 -04:00
p_monsalvete
adf62d56db fixed bug not printing results when building not processed 2023-05-02 12:39:51 -04:00
jgavalda
94d933be4e Incorporation of outputs of all the variables in excel, and pandas dataframe data for yearly values in the cashflow (only capital and reposition, pending operational, end of life and maintenance) 2023-05-01 16:38:44 -04:00
14 changed files with 6950 additions and 1307560 deletions
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65
README.md
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@ -1,13 +1,25 @@
# monthly_energy_balance_workflow
This tool calculates the monthly energy balance for each building in a given region.
It provides as results the energy demand and consumption separated in:
- Heating
- Cooling
- Domestic Hot Water
Also, electrical demand of:
- Lighting
- Other appliances
And heating and cooling peak loads.
All these results are provided both, monthly a yearly based.
The following assumptions are taken:
- No building in the region have attics
- All buildings have basement but those are not heated
- The construction details come from nrel
- The usage details come from comnet
- The heated volume is 85% of the total volume, assuming that the rest is occupied by internal walls
- The construction details come from nrcan
- The usage details come from nrcan
- The system details come from the Montreal Custom catalog
#### Dependencies
@ -18,50 +30,3 @@ You will need to install the following dependencies in your computer in order to
+ Simplified Radiosity Algorithm (mail to: guillermo.gutierrezmorote@concordia.ca)
After installing these tools you should include their paths in Path.
## installation
##### Linux / Mac
Open a terminal and run the following commands.
```
$ mkdir monthly_enery_balance_workflow
$ cd monthly_enery_balance_workflow
$ git clone https://binarycat.org/git/PMAU/monthly_enery_balance_workflow.git
MISSING CONNECTING TO HUB, SIMLPIFIED_RADIOSITY_ALGORITHM AND MONTHLYENERGYBALANCE!!!!!!!!!
$ python -m pip install -r ../requirements.txt
```
##### Windows
Open a terminal and run the following commands.
```
c:\> mkdir monthly_enery_balance_workflow
c:\> cd monthly_enery_balance_workflow
c:\> git clone https://binarycat.org/git/PMAU/monthly_enery_balance_workflow.git
MISSING CONNECTING TO HUB, SIMLPIFIED_RADIOSITY_ALGORITHM AND MONTHLYENERGYBALANCE!!!!!!!!!
c:\> python.exe -m pip install -r ../requirements.txt
```
#### usage
##### Linux / Mac
Open a terminal and run the following command
```
$ python main.py
```
##### Windows
Open a terminal and run the following command.
```
c:\> python.exe main.py
```

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409
input_files/one_building_in_kelowna.gml
View File

@ -1,409 +0,0 @@
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</bldg:boundedBy>
</bldg:Building>
</core:cityObjectMember>
</core:CityModel>

1541
input_files/output_buildings.geojson Normal file
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1254205
input_files/part.geojson
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2473
input_files/summerschool_all_buildings.geojson Normal file
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294
input_files/summerschool_one_building.geojson Normal file
View File

@ -0,0 +1,294 @@
{
"type": "FeatureCollection",
"features": [
{
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"id": 12,
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2473
input_files/test_buildings.geojson Normal file
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File diff suppressed because it is too large Load Diff

51
main.py
View File

@ -3,22 +3,19 @@ from pathlib import Path
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.energy_systems_factory import EnergySystemsFactory
import hub.helpers.constants as cte
from results import Results
from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
from sra_engine import SraEngine
try:
file_path = (Path(__file__).parent / 'input_files' / 'selected_building.geojson')
climate_reference_city = 'Montreal'
weather_file = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
weather_format = 'epw'
file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
construction_format = 'nrcan'
usage_format = 'nrcan'
attic_heated_case = 0
basement_heated_case = 1
energy_systems_format = 'montreal_custom'
out_path = (Path(__file__).parent / 'output_files')
tmp_folder = (Path(__file__).parent / 'tmp')
@ -26,36 +23,44 @@ try:
print('[simulation start]')
city = GeometryFactory('geojson',
path=file_path,
height_field='building_height',
year_of_construction_field='ANNEE_CONS',
function_field='CODE_UTILI',
height_field='height',
year_of_construction_field='year_of_construction',
function_field='function',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
city.climate_reference_city = climate_reference_city
city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
print(f'city created from {file_path}')
WeatherFactory(weather_format, city, file_name=weather_file).enrich()
print('enrich weather... done')
ConstructionFactory(construction_format, city).enrich()
print('enrich constructions... done')
UsageFactory(usage_format, city).enrich()
print('enrich usage... done')
i = 1
x = len(city.buildings)
for building in city.buildings:
if i < x:
building.energy_systems_archetype_name = 'system 1 gas'
else:
building.energy_systems_archetype_name = 'system 6 gas'
i = i + 1
EnergySystemsFactory(energy_systems_format, city).enrich()
print('enrich systems... done')
print('exporting:')
sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
SraEngine(city, sra_file, tmp_folder, weather_file)
# Assign radiation to the city
SraEngine(city, tmp_folder)
print(' sra processed...')
for building in city.buildings:
building.attic_heated = attic_heated_case
building.basement_heated = basement_heated_case
MonthlyEnergyBalanceEngine(city, tmp_folder)
print(' insel processed...')
for building in city.buildings:
for energy_system in building.energy_systems:
if cte.HEATING in energy_system.demand_types:
energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
if cte.COOLING in energy_system.demand_types:
energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
print('importing results:')
results = Results(city, out_path)
results.print()
print('print results...')
print('results printed...')
print('[simulation end]')

2
monthly_energy_balance_engine.py
View File

@ -15,7 +15,7 @@ class MonthlyEnergyBalanceEngine:
self._file_path = file_path
EnergyBuildingsExportsFactory('insel_monthly_energy_balance', self._city, self._file_path).export()
self._run()
ResultFactory('insel_meb', self._city, self._file_path).enrich()
ResultFactory('insel_monthly_energy_balance', self._city, self._file_path).enrich()
def _run(self):
"""

144
results.py
View File

@ -9,34 +9,125 @@ class Results:
self._path = path
def print(self):
print_results = None
file = 'city name: ' + self._city.name + '\n'
for building in self._city.buildings:
if cte.MONTH in building.heating.keys():
heating_results = building.heating[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
cooling_results = building.cooling[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
lighting_results = building.lighting_electrical_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
appliances_results = building.appliances_electrical_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
if cte.MONTH in building.heating_demand.keys():
heating_results = building.heating_demand[cte.MONTH]
else:
array = [None] * 12
heating_results = pd.DataFrame(array, columns=[f'{building.name} heating Wh'])
cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling Wh'])
lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
if print_results is None:
print_results = heating_results
heating_results = [None] * 12
if cte.MONTH in building.cooling_demand.keys():
cooling_results = building.cooling_demand[cte.MONTH]
else:
print_results = pd.concat([print_results, heating_results], axis='columns')
print_results = pd.concat([print_results,
cooling_results,
lighting_results,
appliances_results,
dhw_results], axis='columns')
cooling_results = [None] * 12
if cte.MONTH in building.lighting_electrical_demand.keys():
lighting_results = building.lighting_electrical_demand[cte.MONTH]
else:
lighting_results = [None] * 12
if cte.MONTH in building.appliances_electrical_demand.keys():
appliances_results = building.appliances_electrical_demand[cte.MONTH]
else:
appliances_results = [None] * 12
if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]
else:
dhw_results = [None] * 12
if cte.MONTH in building.heating_consumption.keys():
heating_consumption_results = building.heating_consumption[cte.MONTH]
else:
heating_consumption_results = [None] * 12
if cte.MONTH in building.cooling_consumption.keys():
cooling_consumption_results = building.cooling_consumption[cte.MONTH]
else:
cooling_consumption_results = [None] * 12
if cte.MONTH in building.domestic_hot_water_consumption.keys():
dhw_consumption_results = building.domestic_hot_water_consumption[cte.MONTH]
else:
dhw_consumption_results = [None] * 12
if cte.MONTH in building.heating_peak_load.keys():
heating_peak_load_results = building.heating_peak_load[cte.MONTH]
else:
heating_peak_load_results = [None] * 12
if cte.MONTH in building.cooling_peak_load.keys():
cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
else:
cooling_peak_load_results = [None] * 12
heating = 0
cooling = 0
for system in building.energy_systems:
for demand_type in system.demand_types:
if demand_type == cte.HEATING:
heating = 1
if demand_type == cte.COOLING:
cooling = 1
if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
peak_lighting = 0
peak_appliances = 0
thermal_zone = building.thermal_zones_from_internal_zones[0]
lighting = thermal_zone.lighting
for schedule in lighting.schedules:
for value in schedule.values:
if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
peak_lighting = value * lighting.density * thermal_zone.total_floor_area
appliances = thermal_zone.appliances
for schedule in appliances.schedules:
for value in schedule.values:
if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
peak_appliances = value * appliances.density * thermal_zone.total_floor_area
monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
conditioning_peak = []
for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
else:
conditioning_peak.append(heating * value)
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
electricity_peak_load_results = monthly_electricity_peak
else:
electricity_peak_load_results = [None] * 12
if cte.MONTH in building.onsite_electrical_production.keys():
monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
onsite_electrical_production = monthly_onsite_electrical_production
else:
onsite_electrical_production = [None] * 12
if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
else:
extra_electrical_consumption = [None] * 12
columns_names = [f'{building.name} heating demand J',
f'{building.name} cooling demand J',
f'{building.name} lighting demand J',
f'{building.name} appliances demand J',
f'{building.name} domestic hot water demand J',
f'{building.name} heating consumption J',
f'{building.name} cooling consumption J',
f'{building.name} domestic hot water consumption J',
f'{building.name} heating peak load W',
f'{building.name} cooling peak load W',
f'{building.name} electricity peak load W',
f'{building.name} onsite electrical production J',
f'{building.name} extra electrical consumption J'
]
print_results = pd.DataFrame([heating_results,
cooling_results,
lighting_results,
appliances_results,
dhw_results,
heating_consumption_results,
cooling_consumption_results,
dhw_consumption_results,
heating_peak_load_results,
cooling_peak_load_results,
electricity_peak_load_results,
onsite_electrical_production,
extra_electrical_consumption]).T
print_results.columns = columns_names
file += '\n'
file += f'name: {building.name}\n'
file += f'year of construction: {building.year_of_construction}\n'
@ -46,11 +137,10 @@ class Results:
file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
else:
file += f'storeys: n/a\n'
file += f'heated_volume: {0.85 * building.volume}\n'
file += f'volume: {building.volume}\n'
full_path_results = Path(self._path / 'demand.csv').resolve()
print_results.to_csv(full_path_results, na_rep='null')
full_path_results = Path(self._path / f'demand_{building.name}.csv').resolve()
print_results.to_csv(full_path_results, na_rep='null')
full_path_metadata = Path(self._path / 'metadata.csv').resolve()
with open(full_path_metadata, 'w') as metadata_file:
metadata_file.write(file)

16
sra_engine.py
View File

@ -7,21 +7,19 @@ from hub.imports.results_factory import ResultFactory
class SraEngine:
def __init__(self, city, file_path, output_file_path, weather_file):
def __init__(self, city, output_file_path):
"""
SRA class
:param file_path: _sra.xml file path
:param city: City
:param output_file_path: path to output the sra calculation
"""
self._city = city
self._file_path = file_path
self._output_file_path = output_file_path
self._weather_file = weather_file
if platform.system() == 'Linux':
self._executable = 'citysim_sra'
self._executable = 'sra'
elif platform.system() == 'Windows':
self._executable = 'shortwave_integer'
ExportsFactory('sra', self._city, output_file_path, weather_file=self._weather_file, weather_format='epw').export()
self._executable = 'sra.exe'
ExportsFactory('sra', self._city, output_file_path).export()
self._run()
ResultFactory('sra', self._city, output_file_path).enrich()
@ -30,6 +28,8 @@ class SraEngine:
Calls the software
"""
try:
subprocess.run([self._executable, str(self._file_path)], stdout=subprocess.DEVNULL)
subprocess.run([self._executable,
(self._output_file_path / f'{self._city.name}_sra.xml')],
stdout=subprocess.DEVNULL)
except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
raise Exception(error)

0
test_problems Normal file
View File