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_heatpump_cop():
# get weather data
try_data, loc = tsib.readTRY()
# get cop profile
CoP = tsib.simHeatpump(try_data["T"], COP_limit=6.)
assert CoP.max() <= 6.0
assert CoP.min() >= 0.0
def test_pvlib():
starttime = time.time()
# Run PV simulation with Pv_lib from Sandia (default values)
try_data, loc = tsib.readTRY()
tmy_data = tsib.TRY2TMY(try_data)
specific_load, space_coverage = tsib.simPhotovoltaic(tmy_data)
print("Summary of specific load profile [kW/kWp]:")
print(specific_load.describe())
print("Space coverage [m^2/kWp]: " + str(space_coverage))
print("Profile generation took " + str(time.time() - starttime) + "s")
def test_solarthermal():
# read weather data
try_data, loc = tsib.readTRY(try_num=4)
# format to tmy format
tmy_data = tsib.TRY2TMY(try_data)
# get solar thermal potential
spec_load_st = sol.simSolarThermal(
tmy_data,
latitude=loc['latitude'],
longitude=loc['longitude'],
surface_tilt=30,
surface_azimuth=180,
)
# expected yield in kWh/sqm/a
np.testing.assert_array_almost_equal(spec_load_st.sum(),
584.956523695128,
decimal=0)
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()
def test_fireplace():
# get weather data
try_data, loc = tsib.readTRY()
n_occs = 3
occupancy_data = tsib.simSingleHousehold(n_occs, 2010, get_hot_water=True, resample_mean=True)
# test for different seeds
fullloadhours = 450
# test for different seeds
for seed in range(5):
fireplaceprofile = tsib.simFireplace(
try_data["T"],
occupancy_data["OccActive"] / n_occs,
n_ovens=1,
T_oven_on=5,
t_cool=5.0,
fullloadSteps=fullloadhours,
seed=seed,
)
assert fireplaceprofile.sum() < fullloadhours + 100
assert fireplaceprofile.sum() > fullloadhours - 100
import tsib
# %%
import multiprocessing as mp
# %% [markdown]
# Number of household size
# %%
HH_SIZE = [1, 2, 3, 4, 5]
# %% [markdown]
# Get time series for Potsdam
# %%
weather, loc = tsib.readTRY(try_num=4)
# %% [markdown]
# Get a time dataframe for performance checking
# %%
timeFrame = pd.DataFrame(0,
columns=['Start time', 'End time', 'Duration [s]'],
index=HH_SIZE)
# %% [markdown]
# Generate for all seeds the profiles, if not existant
# %%
SEEDS = [x for x in range(tsib.buildingmodel.TOTAL_PROFILE_NUM + 1)]
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
# %% [markdown]
# # Test weather data and pvlib
# %%
import matplotlib.pyplot as plt
# %matplotlib inline
# %load_ext autoreload
# %autoreload 2
import tsib
# %% [markdown]
# ## 1. Simulate PV with TRY data
# %%
try_data, loc = tsib.readTRY(5)
# %%
tmy_data = tsib.TRY2TMY(try_data)
# %%
pv_try, space_cov = tsib.simPhotovoltaic(tmy_data,
latitude=loc['latitude'],
longitude=loc['longitude'],
losses=0.1,
integrateInverter=True)
# %%
pv_try.sum()
# %%