def heat_example(testmode=False,
ann_demands_per_type=None):
if ann_demands_per_type is None:
ann_demands_per_type = {'efh': 25000,
'mfh': 80000,
'ghd': 140000}
holidays = {
datetime.date(2010, 5, 24): 'Whit Monday',
datetime.date(2010, 4, 5): 'Easter Monday',
datetime.date(2010, 5, 13): 'Ascension Thursday',
datetime.date(2010, 1, 1): 'New year',
datetime.date(2010, 10, 3): 'Day of German Unity',
datetime.date(2010, 12, 25): 'Christmas Day',
datetime.date(2010, 5, 1): 'Labour Day',
datetime.date(2010, 4, 2): 'Good Friday',
datetime.date(2010, 12, 26): 'Second Christmas Day'}
# Create DataFrame for 2010
demand = pd.DataFrame(
index=pd.date_range(datetime.datetime(2010, 1, 1, 0),
periods=8760, freq='H'))
# Single family house (efh: Einfamilienhaus)
if "efh" in ann_demands_per_type:
demand['efh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='EFH',
building_class=1, wind_class=1,
annual_heat_demand=ann_demands_per_type['efh'],
name='EFH').get_bdew_profile()
# Multi family house (mfh: Mehrfamilienhaus)
if "mfh" in ann_demands_per_type:
demand['mfh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='MFH',
building_class=2, wind_class=0,
annual_heat_demand=ann_demands_per_type['mfh'],
name='MFH').get_bdew_profile()
# Industry, trade, service (ghd: Gewerbe, Handel, Dienstleistung)
if "ghd" in ann_demands_per_type:
demand['ghd'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='ghd', wind_class=0,
annual_heat_demand=ann_demands_per_type['ghd'],
name='ghd').get_bdew_profile()
if not testmode:
if plt is not None:
# Plot demand of building
ax = demand.plot()
ax.set_xlabel("Date")
ax.set_ylabel("Heat demand in kW")
plt.show()
else:
print('Annual consumption: \n{}'.format(demand.sum()))
return demand
def heat_example():
holidays = {
datetime.date(2010, 5, 24): 'Whit Monday',
datetime.date(2010, 4, 5): 'Easter Monday',
datetime.date(2010, 5, 13): 'Ascension Thursday',
datetime.date(2010, 1, 1): 'New year',
datetime.date(2010, 10, 3): 'Day of German Unity',
datetime.date(2010, 12, 25): 'Christmas Day',
datetime.date(2010, 5, 1): 'Labour Day',
datetime.date(2010, 4, 2): 'Good Friday',
datetime.date(2010, 12, 26): 'Second Christmas Day'
}
# Create DataFrame for 2010
demand = pd.DataFrame(index=pd.date_range(
pd.datetime(2010, 1, 1, 0), periods=8760, freq='H'))
demand['efh'] = bdew.HeatBuilding(demand.index,
holidays=holidays,
temperature=temperature,
shlp_type='EFH',
building_class=1,
wind_class=1,
annual_heat_demand=25000,
name='EFH').get_bdew_profile()
demand['mfh'] = bdew.HeatBuilding(demand.index,
holidays=holidays,
temperature=temperature,
shlp_type='MFH',
building_class=2,
wind_class=0,
annual_heat_demand=80000,
name='MFH').get_bdew_profile()
demand['ghd'] = bdew.HeatBuilding(demand.index,
holidays=holidays,
temperature=temperature,
shlp_type='ghd',
wind_class=0,
annual_heat_demand=140000,
name='ghd').get_bdew_profile()
# Plot demand of building
ax = demand.plot()
ax.set_xlabel("Date")
ax.set_ylabel("Heat demand in MW")
plt.show()
def get_heat_profile_from_demandlib(temperature,
annual_demand,
sector,
year,
build_class=1):
cal = Germany()
holidays = dict(cal.holidays(year))
if 'efh' in sector:
shlp_type = 'EFH'
elif 'mfh' in sector:
shlp_type = 'MFH'
elif 'domestic' in sector:
shlp_type = 'MFH'
elif 'retail' in sector:
shlp_type = 'ghd'
build_class = 0
elif 'industrial' in sector:
shlp_type = 'ghd'
build_class = 0
else:
raise AttributeError('"{0}" is an unknown sector.'.format(sector))
return bdew.HeatBuilding(temperature.index,
holidays=holidays,
temperature=temperature,
shlp_type=shlp_type,
wind_class=0,
building_class=build_class,
annual_heat_demand=annual_demand,
name=sector,
ww_incl=True).get_bdew_profile()
def prepare_timeseries_demand_heat(year, bdew_parameters, temperature,
output_file):
"""
Creates synthetic heat profiles using the BDEW method.
"""
# get holidays for germany
cal = Germany()
holidays = dict(cal.holidays(year))
# create a DataFrame to hold the timeseries
demand = pd.DataFrame(index=pd.date_range(
pd.datetime(year, 1, 1, 0), periods=8760, freq='H'))
demand = pd.DataFrame(index=temperature.index)
for key, param in bdew_parameters.items():
demand[key] = bdew.HeatBuilding(
demand.index,
holidays=holidays,
temperature=temperature,
shlp_type=key,
building_class=param['building_class'],
wind_class=param['wind_class'],
annual_heat_demand=param['annual_demand'],
name=key).get_bdew_profile()
# save heat demand time series
demand.sum(axis=1).to_csv(output_file)
def get_heat_profile_from_demandlib(temperature,
annual_demand,
sector,
year,
build_class=1):
"""
Create an hourly load profile from the annual demand using the demandlib.
Parameters
----------
temperature : pandas.Series
annual_demand : float
sector : str
year : int
build_class : int
Returns
-------
pandas.DataFrame
Examples
--------
>>> temperature=pd.Series(list(range(50)), index=pd.date_range(
... '2014-05-03 12:00', periods=50, freq='h'))
>>> temperature = 10 + temperature * 0.1
>>> hp=get_heat_profile_from_demandlib(
... temperature, 5345, 'retail', 2014)
>>> int(round(hp.sum()))
5302
"""
cal = Germany()
holidays = dict(cal.holidays(year))
if "efh" in sector:
shlp_type = "EFH"
elif "mfh" in sector:
shlp_type = "MFH"
elif "domestic" in sector:
shlp_type = "MFH"
elif "retail" in sector:
shlp_type = "ghd"
build_class = 0
elif "industrial" in sector:
shlp_type = "ghd"
build_class = 0
else:
raise AttributeError('"{0}" is an unknown sector.'.format(sector))
return bdew.HeatBuilding(
temperature.index,
holidays=holidays,
temperature=temperature,
shlp_type=shlp_type,
wind_class=0,
building_class=build_class,
annual_heat_demand=annual_demand,
name=sector,
ww_incl=True,
).get_bdew_profile()
def heat_example():
"""holidays = {
datetime.date(2010, 5, 24): 'Whit Monday',
datetime.date(2010, 4, 5): 'Easter Monday',
datetime.date(2010, 5, 13): 'Ascension Thursday',
datetime.date(2010, 1, 1): 'New year',
datetime.date(2010, 10, 3): 'Day of German Unity',
datetime.date(2010, 12, 25): 'Christmas Day',
datetime.date(2010, 5, 1): 'Labour Day',
datetime.date(2010, 4, 2): 'Good Friday',
datetime.date(2010, 12, 26): 'Second Christmas Day'}
"""
# Create DataFrame for 2010
demand = pd.DataFrame(index=pd.date_range(
pd.datetime(2016, 1, 1, 0), periods=8760, freq='H'))
"""
# Single family house (efh: Einfamilienhaus)
demand['efh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='EFH',
building_class=1, wind_class=1, annual_heat_demand=25000,
name='EFH').get_bdew_profile()
# Multi family house (mfh: Mehrfamilienhaus)
demand['mfh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='MFH',
building_class=2, wind_class=0, annual_heat_demand=80000,
name='MFH').get_bdew_profile()
"""
# Industry, trade, service (ghd: Gewerbe, Handel, Dienstleistung)
demand['DH'] = bdew.HeatBuilding(demand.index,
holidays=holidays,
temperature=temperature,
shlp_type='ghd',
wind_class=0,
annual_heat_demand=11900,
name='DH').get_bdew_profile()
if plt is not None:
# Plot demand of building
ax = demand.plot()
ax.set_xlabel("Date")
ax.set_ylabel("Heat demand in kW")
plt.show()
print('Annual consumption: \n{}'.format(demand.sum()))
else:
print('Annual consumption: \n{}'.format(demand.sum()))
demand.to_csv('C:/Users/lovisaax/Desktop/data/industrial_heat_load.csv',
index_label='Time (UTC)',
header='DH')
def create_standardised_heat_load_profile(shlp, year):
"""
Parameters
----------
shlp : dict
year : int
Returns
-------
pandas.DataFrame
"""
avg_temp_berlin = (reegis_tools.coastdat.federal_state_average_weather(
year, 'temp_air')['BE'])
# Calculate the average temperature in degree Celsius
temperature = avg_temp_berlin - 272.15
# Fetch the holidays of Germany from the workalendar package
cal = Germany()
holidays = dict(cal.holidays(year))
fuel_list = shlp[list(shlp.keys())[0]]['demand'].index
profile_fuel = pd.DataFrame()
for fuel in fuel_list:
fuel_name = fuel.replace('frac_', '')
profile_type = pd.DataFrame()
for shlp_type in shlp.keys():
shlp_name = str(shlp_type)
profile_type[fuel_name + '_' + shlp_name] = bdew.HeatBuilding(
temperature.index, holidays=holidays, temperature=temperature,
shlp_type=shlp_type, wind_class=0,
building_class=shlp[shlp_type]['build_class'],
annual_heat_demand=shlp[shlp_type]['demand'][fuel],
name=fuel_name + shlp_name, ww_incl=True).get_bdew_profile()
# for district heating the systems the profile will not be summed up
# but kept as different profiles ('district_heating_' + shlp_name).
if fuel_name == 'district_heating':
for n in profile_type.columns:
profile_fuel[n] = profile_type[n]
else:
profile_fuel[fuel_name] = profile_type.sum(axis=1)
return profile_fuel
def call_heat_demandlib(region, time_index, **kwargs):
'''
Calls the demandlib and creates an object that includes the demand
timeseries.
Required Parameters
-------------------
demand : Sink object
region : Region object
'''
load_profile = bdew.HeatBuilding(
time_index,
holidays=kwargs.get('holidays', None),
temperature=region.temp,
shlp_type=kwargs.get('shlp_type', None),
building_class=(region.building_class
if region.building_class is not None else 0),
wind_class=region.wind_class,
annual_heat_demand=kwargs.get('annual_heat_demand', None),
name=kwargs.get('shlp_type', None),
ww_incl=kwargs.get('ww_incl', True)).get_bdew_profile()
return load_profile
def create_standardised_heat_load_profile(shlp, year):
"""
Parameters
----------
shlp : dict
year : int
Returns
-------
pandas.DataFrame
"""
avg_temp_berlin = (reegis.coastdat.federal_state_average_weather(
year, 'temp_air')['BE'])
# Calculate the average temperature in degree Celsius
temperature = avg_temp_berlin - 272.15
# Fetch the holidays of Germany from the workalendar package
cal = Germany()
holidays = dict(cal.holidays(year))
profile_type = pd.DataFrame()
for shlp_type in shlp.keys():
shlp_name = str(shlp_type)
profile_type[shlp_name] = bdew.HeatBuilding(
temperature.index,
holidays=holidays,
temperature=temperature,
shlp_type=shlp_type,
wind_class=0,
building_class=shlp[shlp_type]['build_class'],
annual_heat_demand=1000,
name=shlp_name,
ww_incl=True).get_bdew_profile()
return profile_type
def calculate_heat_demand(
country,
lat,
lon,
storeys,
year,
weather,
column,
static_inputs_directory=None,
user_inputs_pvcompare_directory=None,
user_inputs_mvs_directory=None,
):
r"""
Calculates heat demand profile for `storeys`, `country`, `year`.
The heat demand of either space heating or space heating and warm water
is calculated for a given number of houses with a given
number of storeys in a certain country and year.
In order to take heat demand from warm water into account the parameter `include warm water`
in pvcompare’s input file 'building_parameters.csv' is set to `True`.
For further information regarding the calculation of the heat demand profile
see `Heat demand <https://pvcompare.readthedocs.io/en/latest/model_assumptions.html#heat-demand>`_.
Parameters
----------
country: str
The country's name has to be in English and with capital first letter.
storeys: int
Number of storeys of the houses.
year: int
Year for which heat demand time series is calculated. Year can be
chosen between 2008 and 2018.
column: str
name of the demand
weather: :pandas:`pandas.DataFrame<frame>`
hourly weather data frame with the columns:
time, latitude, longitude, wind_speed, temp_air, ghi, dhi, dni,
precipitable_water.
static_inputs_directory: str or None
Directory of the pvcompare static inputs. If None,
`constants.DEFAULT_STATIC_INPUTS_DIRECTORY` is used.
Default: None.
user_inputs_pvcompare_directory: str or None
Path to user input directory. If None,
`constants.DEFAULT_USER_INPUTS_PVCOMPARE_DIRECTORY` is used.
Default: None.
user_inputs_mvs_directory: str or None
Path to input directory containing files that describe the energy
system and that are an input to MVS. If None,
`constants.DEFAULT_USER_INPUTS_MVS_DIRECTORY` is used.
Default: None.
Returns
-------
shifted_heat_demand : :pandas:`pandas.DataFrame<frame>`
Hourly heat demand time series.
"""
if static_inputs_directory == None:
static_inputs_directory = constants.DEFAULT_STATIC_INPUTS_DIRECTORY
if user_inputs_pvcompare_directory == None:
user_inputs_pvcompare_directory = (
constants.DEFAULT_USER_INPUTS_PVCOMPARE_DIRECTORY)
if user_inputs_mvs_directory == None:
user_inputs_mvs_directory = constants.DEFAULT_USER_INPUTS_MVS_DIRECTORY
# load workelendar for country
cal = get_workalendar_class(country)
holidays = dict(cal.holidays(int(year)))
# define temperature
temp = weather["temp_air"]
# Create DataFrame for demand timeseries
demand = pd.DataFrame(index=pd.date_range(
pd.datetime(int(year), 1, 1, 0), periods=temp.count(), freq="H"))
# calculate annual demand
# The annual heat consumption is calculated by adding up the total
# consumption for SH and WH and subtracting the electrical consumption of
# SH and WH for a country
bp = pd.read_csv(
os.path.join(user_inputs_pvcompare_directory,
"building_parameters.csv"),
index_col=0,
)
filename_total_SH = os.path.join(static_inputs_directory,
bp.at["filename_total_SH", "value"])
filename_total_WH = os.path.join(static_inputs_directory,
bp.at["filename_total_WH", "value"])
population_per_storey = int(bp.at["population per storey", "value"])
number_of_houses = int(bp.at["number of houses", "value"])
population = storeys * population_per_storey * number_of_houses
total_SH = pd.read_excel(filename_total_SH, header=1, index_col=0)
total_WH = pd.read_excel(filename_total_WH, header=1, index_col=0)
# load population
filename_population = bp.at["filename_country_population", "value"]
filename1 = os.path.join(static_inputs_directory, filename_population)
populations = pd.read_csv(filename1, index_col=0, sep=",")
# convert TWh in kWh
# Heat demand of residential space heating
heat_demand = total_SH.at[country, year] * 10**9
annual_heat_demand_per_population = (
heat_demand / float(populations.at[country, str(year)])) * population
# Heat demand of residential water heating
heat_demand_ww = total_WH.at[country, year] * 10**9
annual_heat_demand_ww_per_population = (heat_demand_ww / float(
populations.at[country, str(year)])) * population
# Multi family house (mfh: Mehrfamilienhaus)
include_warm_water = eval(bp.at["include warm water", "value"])
# Calculate heat demand only for space heating
demand["h0"] = bdew.HeatBuilding(
demand.index,
holidays=holidays,
temperature=temp,
shlp_type="MFH",
building_class=2,
wind_class=0,
annual_heat_demand=annual_heat_demand_per_population,
name="MFH",
ww_incl=False, # This must be False. Warm water calc follows
).get_bdew_profile()
# Read heating limit temperature
heating_lim_temp = int(bp.at["heating limit temperature", "value"])
if include_warm_water:
# Calculate annual heat demand with warm water included
annual_heat_demand_per_population = (
annual_heat_demand_per_population +
annual_heat_demand_ww_per_population)
# Create a copy of demand dataframe for warm water calculations
demand_ww_calc = demand.copy()
# Get total heat demand with warm water
demand_ww_calc["h0_ww"] = bdew.HeatBuilding(
demand_ww_calc.index,
holidays=holidays,
temperature=temp,
shlp_type="MFH",
building_class=2,
wind_class=0,
annual_heat_demand=annual_heat_demand_per_population,
name="MFH",
ww_incl=True,
).get_bdew_profile()
# Calculate hourly difference in demand between space heating and space heating with warm water
demand_ww_calc[
"h0_diff"] = demand_ww_calc["h0_ww"] - demand_ww_calc["h0"]
# for space heating *only* adjust the heat demand so there is no demand if daily mean temperature
# is above the heating limit temperature
demand["h0"] = adjust_heat_demand(temp, heating_lim_temp, demand["h0"])
# Add the heat demand for warm water to the adjusted space heating demand
demand["h0"] = demand["h0"] + demand_ww_calc["h0_diff"]
else:
# Adjust the heat demand so there is no demand if daily mean temperature
# is above the heating limit temperature
demand["h0"] = adjust_heat_demand(temp, heating_lim_temp, demand["h0"])
shifted_heat_demand = shift_working_hours(country=country, ts=demand)
shifted_heat_demand.rename(columns={"h0": "kWh"}, inplace=True)
if user_inputs_mvs_directory is None:
user_inputs_mvs_directory = constants.DEFAULT_USER_INPUTS_MVS_DIRECTORY
timeseries_directory = os.path.join(user_inputs_mvs_directory,
"time_series/")
logging.info("The electrical load profile is completely calculated and "
"being saved under %s." % timeseries_directory)
# define the name of the output file of the time series
h_demand_csv = f"heat_load_{year}_{lat}_{lon}_{storeys}.csv"
filename = os.path.join(timeseries_directory, h_demand_csv)
shifted_heat_demand.to_csv(filename, index=False)
# save the file name of the time series and the nominal value to
# mvs_inputs/elements/csv/energyProduction.csv
check_inputs.add_file_name_to_energy_consumption_file(
column=column,
ts_filename=h_demand_csv,
user_inputs_mvs_directory=user_inputs_mvs_directory,
)
return shifted_heat_demand
def heating_systems(esystem, dfull, add_elec, p):
power_plants = prepare_data.chp_berlin(p)
time_index = esystem.time_idx
temperature_path = '/home/uwe/rli-lokal/git_home/demandlib/examples'
temperature_file = temperature_path + '/example_data.csv'
temperature = pd.read_csv(temperature_file)['temperature']
sli = pd.Series(list(temperature.loc[:23]), index=list(range(8760, 8784)))
temperature = temperature.append(sli)
temperature = temperature.iloc[0:len(time_index)]
heatbus = dict()
hd = dict()
auxiliary_energy = 0
print(dfull)
for h in dfull.keys():
hd.setdefault(h, 0)
lsink = 'demand_{0}'.format(h)
lbus = 'bus_{0}'.format(h)
ltransf = '{0}'.format(h)
lres_bus = 'bus_{0}'.format(p.heating2resource[h])
for b in dfull[h].keys():
if b.upper() in p.bdew_types:
bc = 0
if b.upper() in ['EFH', 'MFH']:
bc = 1
hd[h] += bdew.HeatBuilding(time_index,
temperature=temperature,
shlp_type=b,
building_class=bc,
wind_class=1,
annual_heat_demand=dfull[h][b],
name=h).get_bdew_profile()
if b.upper() in ['EFH', 'MFH']:
print(h, 'in')
auxiliary_energy += bdew.HeatBuilding(
time_index,
temperature=temperature,
shlp_type=b,
building_class=bc,
wind_class=1,
annual_heat_demand=add_elec[h][b],
name=h).get_bdew_profile()
elif b in [
'i',
]:
hd[h] += pprofiles.IndustrialLoadProfile(
time_index).simple_profile(annual_demand=dfull[h][b])
else:
logging.error('Demandlib typ "{0}" not found.'.format(b))
heatbus[h] = solph.Bus(label=lbus)
solph.Sink(label=lsink,
inputs={
heatbus[h]:
solph.Flow(actual_value=hd[h].div(hd[h].max()),
fixed=True,
nominal_value=hd[h].max())
})
if 'district' not in h:
if lres_bus not in esystem.groups:
solph.Bus(label=lres_bus)
solph.LinearTransformer(
label=ltransf,
inputs={esystem.groups[lres_bus]: solph.Flow()},
outputs={
heatbus[h]:
solph.Flow(nominal_value=hd[h].max(), variable_costs=0)
},
conversion_factors={heatbus[h]: 1})
else:
for pp in power_plants[h].index:
lres_bus = 'bus_' + pp
if lres_bus not in esystem.groups:
solph.Bus(label=lres_bus)
solph.LinearTransformer(
label='pp_chp_{0}_{1}'.format(h, pp),
inputs={esystem.groups[lres_bus]: solph.Flow()},
outputs={
esystem.groups['bus_el']:
solph.Flow(
nominal_value=power_plants[h]['power_el'][pp]),
heatbus[h]:
solph.Flow(
nominal_value=power_plants[h]['power_th'][pp])
},
conversion_factors={
esystem.groups['bus_el']: 0.3,
heatbus[h]: 0.4
})
from matplotlib import pyplot as plt
hd_df = pd.DataFrame(hd)
print(hd_df.sum().sum())
print('z_max:', hd_df['district_z'].max())
print('dz_max:', hd_df['district_dz'].max())
print('z_sum:', hd_df['district_z'].sum())
print('dz_sum:', hd_df['district_dz'].sum())
hd_df.plot(colormap='Spectral')
hd_df.to_csv('/home/uwe/hd.csv')
plt.show()
solph.Sink(label='auxiliary_energy',
inputs={
esystem.groups['bus_el']:
solph.Flow(actual_value=auxiliary_energy.div(
auxiliary_energy.max()),
fixed=True,
nominal_value=auxiliary_energy.max())
})
# Create sinks
# Get normalise demand and maximum value of electricity usage
electricity_usage = electricity.DemandElec(time_index)
normalised_demand, max_demand = electricity_usage.solph_sink(
resample='H', reduce=auxiliary_energy)
sum_demand = normalised_demand.sum() * max_demand
print("delec:", "{:.2E}".format(sum_demand))
solph.Sink(label='elec_demand',
inputs={
esystem.groups['bus_el']:
solph.Flow(actual_value=normalised_demand,
fixed=True,
nominal_value=max_demand)
})
try_weather = pd.read_csv(datapath, skiprows=34, sep='\t', header=None,
decimal=',')
temperature = try_weather[5]
cal = Germany()
holidays = dict(cal.holidays(2010))
# Create DataFrame for 2010
demand = pd.DataFrame(
index=pd.date_range(pd.datetime(2010, 1, 1, 0),
periods=8760, freq='H'))
# Single family house (efh: Einfamilienhaus)
demand['efh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='EFH',
building_class=1, wind_class=1, annual_heat_demand=25000,
name='EFH').get_bdew_profile()
# Multi family house (mfh: Mehrfamilienhaus)
demand['mfh'] = bdew.HeatBuilding(
demand.index, holidays=holidays, temperature=temperature,
shlp_type='MFH',
building_class=2, wind_class=0, annual_heat_demand=80000,
name='MFH').get_bdew_profile()
# Industry, trade, service (ghd: Gewerbe, Handel, Dienstleistung)
# Valid values for slp_types are:
# GMK, GPD, GHA, GBD, GKO, GBH, GGA, GBA, GWA, GGB, GMF, GHD
# https://demandlib.readthedocs.io/en/latest/description.html#heat-profiles
demand['ghd'] = bdew.HeatBuilding(
