def test_scaleHeatDemand():
# get raw building data set
buildingSet = pd.read_csv(os.path.join(tsib.data.PATH, "episcope",
"tabula_DE_wPersons.csv"),
header=0,
index_col=0)
# get a random building ID
ix = 24
ID = buildingSet.index[ix]
# get time series data
try_data, loc = tsib.readTRY(try_num=4)
# parameterize a building
bdgcfg = tsib.BuildingConfiguration({
"ID": ID,
"weatherData": try_data,
"weatherID": "TRY_4",
})
bdgObj = tsib.Building(configurator=bdgcfg)
# get the design heat load
origDesignLoad = bdgObj.thermalmodel.calcDesignHeatLoad()
# scale to a reduced value
bdgObj.thermalmodel.scaleHeatLoad(scale=0.5)
# get updated load
reducedDesignLoad = bdgObj.thermalmodel.calcDesignHeatLoad()
np.testing.assert_almost_equal(reducedDesignLoad / 0.5,
origDesignLoad,
decimal=2)
def test_renewable():
starttime = time.time()
# get raw building data set
buildingSet = pd.read_csv(os.path.join(tsib.data.PATH, "episcope",
"tabula_DE_wPersons.csv"),
header=0,
index_col=0)
# get a random building ID
ix = 24
ID = buildingSet.index[ix]
# get time series data
try_data, loc = tsib.readTRY(try_num=4)
# parameterize a building
bdgcfg = tsib.BuildingConfiguration({
"ID": ID,
"weatherData": try_data,
"weatherID": "TRY_4",
"roofOrientation": 0.0,
"longitude": loc["longitude"],
"latitude": loc["latitude"],
})
# setup a building with the configuration
bdgObj = tsib.Building(configurator=bdgcfg)
# get the renewable profiles to manipulate them
bdgObj.getRenewables()
def test_surround_weather_error_with_dummy():
# parameterize a building
bdgcfg = tsib.BuildingConfiguration(
{
"buildingYear": 1980,
"country": "BE",
"n_persons": 2,
"roofOrientation": 0.0,
"n_apartments": 1,
"weatherData": pd.DataFrame([[0,0,0,]], columns=["DHI", "T", "DNI"]),
"weatherID": "Dummy",
"surrounding": "Detached",
"latitude": 50.,
"longitude": 1.,
}
)
bdg = tsib.Building(configurator=bdgcfg)
return
def test_get_ID():
# parameterize a building
bdgcfg = tsib.BuildingConfiguration({
"refurbishment": False,
"nightReduction": False,
"occControl": False,
"capControl": True,
"n_persons": 2,
"roofOrientation": 0.0,
"n_apartments": 1,
"latitude": 49.,
"longitude": 12.,
})
bdgObj = tsib.Building(configurator=bdgcfg)
print('ID is : ' + str(bdgObj.ID))
return
def test_set_ID():
# parameterize a building
bdgcfg = tsib.BuildingConfiguration({
"buildingYear": 1980,
"n_persons": 2,
"roofOrientation": 0.0,
"n_apartments": 2,
"a_ref": 300.,
"surrounding": "Detached",
"latitude": 52.,
"longitude": 13.,
})
bdgObj = tsib.Building(configurator=bdgcfg)
bdgObj.ID = 'custom'
if not bdgObj.ID == 'custom':
raise ValueError()
return
def test_smoke():
# Read buildings from episcope database
bdgs_df = pd.read_csv(os.path.join("tsib/data/episcope/episcope.csv"),
index_col=1)
# Select random building by building code
bdg_dict = bdgs_df.loc['DE.N.SFH.10.Gen.ReEx.001.001'].to_dict()
# Create building configuration object
# NOTE: The right kwargs have to be created
bdg_cfg = tsib.BuildingConfiguration(bdg_dict)
# Get weather data
try_data, loc = tsib.readTRY()
# Initialize a building object with this configuration for given weather
# NOTE: Weather data seperated from building configuration
bdg_obj = tsib.Building(configurator=bdg_cfg, weather=try_data)
# Calculate loads
bdg_obj.getLoad()
EXPORT_PATH = os.path.join('plots')
# %% [markdown]
# ### Define the building
# %% [markdown]
# Init building
# %%
bdgcfg = tsib.BuildingConfiguration({ 'refurbishment': False, 'nightReduction': True, 'buildingYear': 2005,
'occControl': False, 'capControl': True,'n_persons': 2,
'comfortT_lb': 20.0, 'comfortT_ub': 26.0, 'roofOrientation': 0.0,
'longitude': 7., 'latitude': 54. })
# %%
bdgObj = tsib.Building(configurator = bdgcfg)
# %% [markdown]
# ## Get the load
# %%
bdgObj.get_load()
# %% [markdown]
# Plot
# %%
bdgObj.detailed_results['Heating Load'].plot()
# %%
bdgObj.detailed_results['Heating Load']['20100101':'20100107'].plot()
def test_heatload():
starttime = time.time()
# get raw building data set
buildingSet = pd.read_csv(os.path.join(tsib.data.PATH, "episcope",
"tabula_DE_wPersons.csv"),
header=0,
index_col=0)
# get a random building ID
ix = 24
ID = buildingSet.index[ix]
# get time series data
try_data, loc = tsib.readTRY(try_num=4)
# parameterize a building
bdgcfg = tsib.BuildingConfiguration({
"ID": ID,
"weatherData": try_data,
"weatherID": "TRY_4",
"refurbishment": False,
"nightReduction": False,
"occControl": False,
"capControl": True,
"n_persons": 2,
"comfortT_lb": 20.0,
"comfortT_ub": 26.0,
"roofOrientation": 0.0,
"n_apartments": 1,
"longitude": loc["longitude"],
"latitude": loc["latitude"],
})
# setup a building with the configuration
bdgObj = tsib.Building(configurator=bdgcfg)
# get the occupancy profiles to manipulate them
bdgObj._get_occupancy_profile(bdgObj.cfg)
# manipulate internal gains with tabula mean value
bdgObj.cfg["Q_ig"] = (bdgObj.cfg["Q_ig"] * 15.552 /
(bdgObj.cfg["Q_ig"].sum() / bdgObj.cfg["A_ref"]))
# run simulation
bdgObj.getHeatLoad() # take solver from environment variable
# get specific heat demand
q_sim = bdgObj.timeseries["Heating Load"].sum() / bdgObj.cfg["A_ref"]
# get calculated heat demand by IWU
q_iwu = buildingSet.loc[ID, "q_h_nd"]
print("Profile generation took " + str(time.time() - starttime))
print("Spec. heat demand IWU [kWh/m²/a]: " + str(round(q_iwu)))
print("Spec. heat demand 5R1C [kWh/m²/a]: " + str(round(q_sim)))
if abs(q_sim - q_iwu) > 20:
raise ValueError(
"The difference between simulation and the values listed by the IWU is too high."
)
if ix == 24:
if not round(q_sim) == 197.0:
raise ValueError(
"Different result for mean heat load than expected.")
return
"buildingYear": 1990,
"latitude": 50.0,
"longitude": 8.0,
"n_persons": 2,
"a_ref": 150.,
"n_apartments": 1,
"surrounding": "Detached",
"mean_load": True,
"occControl": False,
})
# %% [markdown]
# Parameterize the building model itself with the configuration
# %%
bdg = tsib.Building(configurator=cfg)
# %% [markdown]
# ### 2. Show the weather data as basis
# %% [markdown]
# Get current time series data. Until here, only weather data is provided.
# %%
bdg.timeseries.head()
# %% [markdown]
# Plot the weather data
# %%
for name in bdg.timeseries:
# %%
for bdg_ix in bdgs_dict:
bdg_cfgs[bdg_ix] = tsib.BuildingConfiguration(bdgs_dict[bdg_ix])
bdg_cfgs_print[bdg_ix] = bdg_cfgs[bdg_ix].getBdgCfg()
# %%
bdg_cfgs_print[0]
# %%
import tsib
# %%
bdgs = {}
# %%
for bdg_ix in bdgs_dict:
bdgs[bdg_ix] = tsib.Building(configurator=bdg_cfgs[bdg_ix])
bdgs[bdg_ix].getLoad()
# %% [markdown]
# ### Plot example results and export to CSV
# %%
bdgs[0].timeseries['Heating Load'].plot()
# %%
bdgs[0].timeseries['Electricity Load'].plot()
# %%
bdgs[0].toCSV(filename="example_result")