def predict_cars_emissions(datasets, variables):
target_year = variables['target_year']
mileage_per_engine_type = datasets['mileage_per_engine_type']
mileage_share_per_engine_type = mileage_per_engine_type.set_index(
['Vehicle', 'Engine']).drop(columns='Sum')
df = prepare_transportation_emissions_dataset()
df = df.loc[df.Vehicle == 'Cars',
['Year', 'CO2e', 'Road']].set_index('Year')
emissions_df = df.pivot(values='CO2e', columns='Road')
df = predict_cars_mileage()
for road in ('Highways', 'Urban'):
df[road + 'Emissions'] = emissions_df[road] / 1000 # -> kt
car_unit_emissions = datasets['car_unit_emissions'].set_index(
['Engine', 'Road'])
elec_df = predict_electricity_emission_factor()
share_df = predict_cars_in_use_by_engine_type()
last_hist_year = df[~df.Forecast].index.max()
# Estimate mileage ratio shares between engine types
share = estimate_mileage_ratios(share_df)
# Estimate emissions per km per engine type
unit_df = car_unit_emissions.reset_index()
unit_df = unit_df.groupby(['Road', 'Engine',
'Class']).mean()['CO2e'].unstack('Class')
unit_df['Year'] = last_hist_year
elec_df = elec_df.loc[elec_df.index >= last_hist_year]
unit_df = estimate_bev_unit_emissions(unit_df, elec_df['EmissionFactor'])
df = calculate_co2e_per_engine_type(df, share, unit_df)
engine_shares = share.sum(axis=1).unstack('Engine')
for engine_type in ('gasoline', 'diesel', 'electric', 'PHEV (gasoline)'):
df[engine_type] = engine_shares[engine_type]
df['Emissions'] = (df['HighwaysEmissions'] + df['UrbanEmissions'])
df['EmissionFactor'] = df['Emissions'] / (
df['Urban'] + df['Highways']) * 1000000000 # g/km
return df
def solar_power_callback(existing_building_perc, new_building_perc):
# First see what the maximum solar production capacity is to set the
# Y axis maximum.
set_variable('solar_power_existing_buildings_percentage', 100)
set_variable('solar_power_new_buildings_percentage', 100)
kwp_max = predict_solar_power_production()
# Then predict with the given percentages.
set_variable('solar_power_existing_buildings_percentage',
existing_building_perc)
set_variable('solar_power_new_buildings_percentage', new_building_perc)
kwp_df = predict_solar_power_production()
ymax = kwp_max.SolarPowerExisting.iloc[-1]
fig_old = generate_solar_power_graph(kwp_df, "Vanhan",
"SolarPowerExisting", ymax, True)
# ymax = kwp_max.SolarPowerNew.iloc[-1]
fig_new = generate_solar_power_graph(kwp_df, "Uuden", "SolarPowerNew",
ymax, False)
fig_tot, ekwpa, nkwpa = generate_solar_power_stacked(kwp_df)
graph = PredictionFigure(sector_name='ElectricityConsumption',
unit_name='kt',
title='Aurinkopaneelien päästövaikutukset',
fill=True)
ef_df = predict_electricity_emission_factor()
kwp_df['NetEmissions'] = -kwp_df['SolarProduction'] * ef_df[
'EmissionFactor'] / 1000
graph.add_series(df=kwp_df,
column_name='NetEmissions',
trace_name='Päästövaikutukset')
fig_emissions = graph.get_figure()
s = kwp_df.SolarProduction
cd = CardDescription()
city_owned = get_variable('building_area_owned_by_org') / 100
cd.set_values(existing_building_perc=existing_building_perc,
org_existing_building_kwp=1000 * ekwpa * city_owned,
others_existing_building_kwp=1000 * ekwpa * (1 - city_owned))
existing_kwpa = cd.render("""
Kun aurinkopaneeleita rakennetaan {existing_building_perc:noround} % kaikesta vanhan
rakennuskannan kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleita
{org_existing_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_existing_building_kwp} kWp aurinkopaneeleita vuodessa.
""")
cd.set_values(new_building_perc=new_building_perc,
org_new_building_kwp=1000 * nkwpa * city_owned,
others_new_building_kwp=1000 * nkwpa * (1 - city_owned))
new_kwpa = cd.render("""
Kun uuteen rakennuskantaan rakennetaan aurinkopaneeleja {new_building_perc:noround} %
kaikesta kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleja
{org_new_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_new_building_kwp} kWp aurinkopaneeleita vuodessa.
""")
forecast = s.iloc[-1] * get_variable('yearly_pv_energy_production_kwh_wp')
sticky = StickyBar(
label='Aurinkosähkön tuotanto',
value=forecast,
unit='GWh',
current_page=page,
)
return [
fig_old, fig_new, fig_tot, fig_emissions, existing_kwpa, new_kwpa,
sticky.render()
]
def refresh_graph_cards(self):
# First see what the maximum solar production capacity is to set the
# Y axis maximum.
set_variable('solar_power_existing_buildings_percentage', 100)
set_variable('solar_power_new_buildings_percentage', 100)
kwp_max_df = predict_solar_power_production()
# Then predict with the given percentages.
existing_card = self.get_card('existing-buildings')
set_variable('solar_power_existing_buildings_percentage', existing_card.get_slider_value())
future_card = self.get_card('new-buildings')
set_variable('solar_power_new_buildings_percentage', future_card.get_slider_value())
df = predict_solar_power_production()
forecast_df = df[df.Forecast]
hist_df = df[~df.Forecast]
years_left = forecast_df.index.max() - hist_df.index.max()
ekwpa = (forecast_df.SolarPowerExisting.iloc[-1] - hist_df.SolarPowerExisting.iloc[-1]) / years_left
nkwpa = forecast_df.SolarPowerNew.iloc[-1] / years_left
cd = CardDescription()
city_owned = get_variable('building_area_owned_by_org') / 100
cd.set_values(
existing_building_perc=existing_card.get_slider_value(),
org_existing_building_kwp=1000 * ekwpa * city_owned,
others_existing_building_kwp=1000 * ekwpa * (1 - city_owned)
)
existing_card.set_description(cd.render("""
Kun aurinkopaneeleita rakennetaan {existing_building_perc:noround} % kaikesta vanhan
rakennuskannan kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleita
{org_existing_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_existing_building_kwp} kWp aurinkopaneeleita vuodessa.
"""))
cd.set_values(
new_building_perc=future_card.get_slider_value(),
org_new_building_kwp=1000 * nkwpa * city_owned,
others_new_building_kwp=1000 * nkwpa * (1 - city_owned)
)
future_card.set_description(cd.render("""
Kun uuteen rakennuskantaan rakennetaan aurinkopaneeleja {new_building_perc:noround} %
kaikesta kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleja
{org_new_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_new_building_kwp} kWp aurinkopaneeleita vuodessa.
"""))
ymax = kwp_max_df.SolarPowerExisting.iloc[-1]
fig = PredictionFigure(
sector_name='ElectricityConsumption', unit_name='MWp',
y_max=ymax, color_scale=2
)
fig.add_series(df=df, trace_name=_('Peak power'), column_name='SolarPowerExisting', color_idx=0)
existing_card.set_figure(fig)
fig = PredictionFigure(
sector_name='ElectricityConsumption', unit_name='MWp',
y_max=ymax, color_scale=2
)
fig.add_series(df=df, trace_name=_('Peak power'), column_name='SolarPowerNew', color_idx=1)
future_card.set_figure(fig)
pv_kwh_wp = get_variable('yearly_pv_energy_production_kwh_wp')
df.SolarPowerNew = df.SolarPowerNew * pv_kwh_wp
df.SolarPowerExisting = df.SolarPowerExisting * pv_kwh_wp
df.loc[~df.Forecast, 'SolarPowerNew'] = np.nan
card = self.get_card('production')
fig = PredictionFigure(
sector_name='ElectricityConsumption', unit_name='GWh',
stacked=True, fill=True, color_scale=2
)
fig.add_series(df=df, trace_name=_('Existing buildings'), column_name='SolarPowerExisting', color_idx=0)
fig.add_series(df=df, trace_name=_('Future buildings'), column_name='SolarPowerNew', color_idx=1)
card.set_figure(fig)
card = self.get_card('emission-impact')
fig = PredictionFigure(
sector_name='ElectricityConsumption', unit_name='kt',
fill=True
)
ef_df = predict_electricity_emission_factor()
df['NetEmissions'] = -df['SolarProduction'] * ef_df['EmissionFactor'] / 1000
fig.add_series(df=df, column_name='NetEmissions', trace_name=_('Emissions'))
card.set_figure(fig)
s = df.SolarProduction
self.solar_production_forecast = s.iloc[-1] * get_variable('yearly_pv_energy_production_kwh_wp')
def refresh_graph_cards(self):
ecard = self.get_card('renovated-per-year')
self.set_variable('geothermal_existing_building_renovation',
ecard.get_slider_value() / 10)
ncard = self.get_card('new-building-installation')
self.set_variable('geothermal_new_building_installation_share',
ncard.get_slider_value())
df = predict_geothermal_production()
print(df)
fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='milj. k-m²',
smoothing=True,
y_max=25,
)
fig.add_series(
df=df,
column_name='GeoBuildingNetAreaExisting',
trace_name='Kerrosala',
)
ecard.set_figure(fig)
org_owned = self.get_variable('building_area_owned_by_org') / 100
cd = CardDescription()
first_forecast = df[df.Forecast].iloc[0]
last_forecast = df.iloc[-1]
last_hist_year = df[~df.Forecast].index.max()
last_forecast_year = df[df.Forecast].index.max()
cd.set_values(
existing_building_perc=self.get_variable(
'geothermal_existing_building_renovation'),
existing_building_area=last_forecast.GeoBuildingNetAreaExisting,
boreholes_org=first_forecast.BoreholesPerYear * org_owned,
boreholes_others=first_forecast.BoreholesPerYear * (1 - org_owned),
borehole_area=last_forecast.BoreholeAreaNeeded,
borehole_depth=self.get_variable('geothermal_borehole_depth'),
new_building_perc=self.get_variable(
'geothermal_new_building_installation_share'),
new_building_area=last_forecast.GeoBuildingNetAreaNew,
geothermal_production=last_forecast.GeoEnergyProduction,
perc_dh=last_forecast.GeoEnergyProduction)
ecard.set_description(
cd.render("""
Kun olemassaolevasta rakennuskannasta remontoidaan {existing_building_perc:noround} %
joka vuosi ja vaihdetaan lämmitystavaksi maalämpö, vuonna {target_year} maalämmöllä
lämmitetään {existing_building_area} milj. kerrosneliömetriä. Skenaarion toteutumiseksi
{org_genitive} pitää rakentaa ensi vuonna {boreholes_org} maalämpökaivoa, kun oletetaan kaivon
syvyydeksi {borehole_depth} m. Muiden pitää rakentaa ensi vuonna {boreholes_others} kaivoa.
"""))
ncard.set_description(
cd.render("""
Kun uudesta rakennuskannasta {new_building_perc:noround} % rakennetaan maalämmöllä,
vuonna {target_year} lämmitetään maalämmöllä {new_building_area} milj. kerrosneliömetriä.
|p|Vanhan ja uuden rakennuskannan maalämpökaivot tarvitsevat silloin yhteensä {borehole_area} km²
pinta-alaa.
"""))
fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='milj. k-m²',
smoothing=True,
y_max=10,
)
fig.add_series(
df=df,
column_name='GeoBuildingNetAreaNew',
trace_name='Kerrosala',
)
ncard.set_figure(fig)
card = self.get_card('geothermal-production')
fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='GWh',
smoothing=True,
fill=True,
stacked=True,
color_scale=2,
)
fig.add_series(
df=df,
column_name='GeoEnergyProductionExisting',
trace_name='Vanha rakennuskanta',
color_idx=0,
)
fig.add_series(
df=df,
column_name='GeoEnergyProductionNew',
trace_name='Uusi rakennuskanta',
color_idx=1,
)
card.set_figure(fig)
card.set_description(
cd.render("""
Vuonna {target_year} maalämpöpumpuilla tuotetaan {geothermal_production} GWh lämpöä.
Skenaariossa se käytetään korvaamaan kaukolämpöä.
"""))
# District heat
dhdf = predict_district_heating_emissions()
card = self.get_card('district-heat-emission-factor')
fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='g/kWh',
smoothing=True,
)
fig.add_series(df=dhdf,
column_name='Emission factor',
trace_name=_('Emission factor'))
card.set_figure(fig)
last_dhdf_hist_year = dhdf[~dhdf.Forecast].index.max()
dhef_target = dhdf.loc[last_forecast_year, 'Emission factor']
dhef_hist = dhdf.loc[last_dhdf_hist_year, 'Emission factor']
cd.set_values(dh_emission_factor_target=dhef_target,
dh_emission_factor_hist=dhef_hist,
perc_change=((dhef_target / dhef_hist) - 1) * 100)
cd.set_variables(last_dhdf_hist_year=last_dhdf_hist_year, )
card.set_description(
cd.render("""
Skenaariossa paikallisella maalämmöllä korvataan pelkästään kaukolämpöä.
Vuonna {target_year} kaukolämmöntuotannon päästökerroin on {dh_emission_factor_target}
g/km. Muutos vuoteen {last_dhdf_hist_year} on {perc_change} %.
"""))
# Electricity
edf = predict_electricity_emission_factor()
card = self.get_card('electricity-emission-factor')
fig = PredictionFigure(
sector_name='ElectricityConsumption',
unit_name='g/kWh',
smoothing=True,
)
fig.add_series(
df=edf,
column_name='EmissionFactor',
trace_name=_('Emission factor'),
)
card.set_figure(fig)
cd.set_values(
heat_pump_el=last_forecast.ElectricityUse,
elef=edf.loc[last_forecast_year].EmissionFactor,
)
card.set_description(
cd.render("""
Vuonna {target_year} maalämpöpumput käyttävät sähköä {heat_pump_el} GWh.
Sähkönkulutuksesta aiheutuvat päästöt määräytyvät sähkönhankinnan päästökertoimen
mukaan ({elef} g/kWh vuonna {target_year}).
"""))
card = self.get_card('emissions')
fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='kt',
smoothing=True,
fill=True,
)
fig.add_series(
df=df,
column_name='NetEmissions',
trace_name=_('Emissions'),
)
card.set_figure(fig)
def solar_power_callback(existing_building_perc, new_building_perc):
# First see what the maximum solar production capacity is to set the
# Y axis maximum.
set_variable('solar_power_existing_buildings_percentage', 100)
set_variable('solar_power_new_buildings_percentage', 100)
kwp_max = predict_solar_power_production()
# Then predict with the given percentages.
set_variable('solar_power_existing_buildings_percentage',
existing_building_perc)
set_variable('solar_power_new_buildings_percentage', new_building_perc)
kwp_df = predict_solar_power_production()
ymax = kwp_max.SolarPowerExisting.iloc[-1]
fig_old = generate_solar_power_graph(kwp_df, "Vanhan",
"SolarPowerExisting", ymax, True)
ymax = kwp_max.SolarPowerNew.iloc[-1]
fig_new = generate_solar_power_graph(kwp_df, "Uuden", "SolarPowerNew",
ymax, False)
fig_tot, ekwpa, nkwpa = generate_solar_power_stacked(kwp_df)
ef_df = predict_electricity_emission_factor()
kwp_df['EmissionReductions'] = ef_df['EmissionFactor'] * kwp_df[
'SolarPowerAll'] / 1000
graph = PredictionGraph(
sector_name='ElectricityConsumption',
unit_name='kt',
title='Aurinkopaneelien päästövähennykset',
)
graph.add_series(kwp_df,
trace_name='Päästövähennykset',
column_name='EmissionReductions')
fig_emissions = graph.get_figure()
s = kwp_df.SolarPowerAll
forecast = s.iloc[-1] * get_variable('yearly_pv_energy_production_kwh_wp')
existing_kwpa = html.Div([
html.Span("Kun aurinkopaneeleita rakennetaan "),
html.Span('%d %%' % existing_building_perc,
className='summary-card__value'),
html.Span(
" kaikesta vanhan rakennuskannan kattopotentiaalista, Helsingin kaupunkikonsernin tulee rakentaa aurinkopaneeleja "
),
html.Span("%d kWp" % (1000 * ekwpa * CITY_OWNED / 100),
className='summary-card__value'),
html.Span(
" vuodessa skenaarion toteutumiseksi. Muiden kuin Helsingin kaupungin tulee rakentaa "
),
html.Span("%d kWp" % (1000 * ekwpa * (100 - CITY_OWNED) / 100),
className='summary-card__value'),
html.Span(" vuodessa."),
])
new_kwpa = html.Div([
html.Span("Kun uuteen rakennuskantaan rakennetaan aurinkopaneeleja "),
html.Span('%d %%' % new_building_perc,
className='summary-card__value'),
html.Span(
" kaikesta kattopotentiaalista, Helsingin kaupunkikonsernin tulee rakentaa aurinkopaneeleja "
),
html.Span("%d kWp" % (1000 * nkwpa * CITY_OWNED / 100),
className='summary-card__value'),
html.Span(
" vuodessa skenaarion toteutumiseksi. Muiden kuin Helsingin kaupungin tulee rakentaa "
),
html.Span("%d kWp" % (1000 * nkwpa * (100 - CITY_OWNED) / 100),
className='summary-card__value'),
html.Span(" vuodessa."),
])
sticky = StickyBar(
label='Aurinkosähkön tuotanto',
value=forecast,
goal=SOLAR_POWER_GOAL,
unit='GWh',
current_page=page,
below_goal_good=False,
)
return [
fig_old, fig_new, fig_tot, fig_emissions, existing_kwpa, new_kwpa,
sticky.render()
]