def predict_electricity_emission_factor(variables, datasets):
"""
df = datasets['ghg_emissions']
df = df[df.Kaupunki == variables['municipality_name']].drop(columns='Kaupunki')
df = df[df.Sektori1 == 'Sähkö']
df['EmissionFactor'] = df['Päästöt'] / df['Energiankulutus'] * 1000
print(df.groupby('Vuosi')[['Päästöt', 'Energiankulutus']].sum())
s = df.groupby('Vuosi')['EmissionFactor'].mean()
print(s)
df = pd.DataFrame(s)
"""
PAST_VALUES = [
214.60, 261.20, 285.80, 349.80, 298.30, 205.00, 307.90, 278.50, 214.40,
229.10, 269.80, 226.90, 166.90, 199.90, 173.10, 134.90, 146.00, 131.40
]
START_YEAR = 2000
df = pd.DataFrame(PAST_VALUES,
index=range(START_YEAR, START_YEAR + len(PAST_VALUES)),
columns=['EmissionFactor'])
df['Forecast'] = False
start_year = find_consecutive_start(df.index)
df = df.reindex(pd.Index(range(start_year, variables['target_year'] + 1)))
df.Forecast = df.Forecast.fillna(True)
df.loc[2030, 'EmissionFactor'] = 70 # g CO2e/kWh
df.loc[2035, 'EmissionFactor'] = 45 # g CO2e/kWh
df['EmissionFactor'] = df['EmissionFactor'].interpolate()
return df
def get_traces_for_series(self, series: PredictionFigureSeries, index: int,
has_multiple_series: bool):
df = series.df
trace_attrs = {}
if self.stacked or self.fill:
if self.stacked and index > 0:
trace_attrs['fill'] = 'tonexty'
else:
trace_attrs['fill'] = 'tozeroy'
if self.fill:
trace_attrs['mode'] = 'none'
else:
trace_attrs['mode'] = 'lines'
if self.allow_nonconsecutive_years:
start_year = df.index.min()
else:
start_year = find_consecutive_start(df.index)
y_column = series.column_name
hist_series = df.loc[~df.Forecast & (df.index >= start_year),
y_column].dropna()
hovertemplate = '%{x}: %{y}'
if self.unit_name:
hovertemplate += ' %s' % self.unit_name
traces = []
line_attrs = dict(width=4)
if self.smoothing:
line_attrs.update(dict(smoothing=1, shape='spline'))
if len(hist_series):
color = series.get_color(forecast=False)
if self.stacked:
trace_attrs['stackgroup'] = 'history'
if self.fill:
trace_attrs['fillcolor'] = color
hist_trace = dict(type='scatter',
x=hist_series.index.astype(str),
y=hist_series,
name=series.trace_name,
hovertemplate=hovertemplate,
line=dict(
color=color,
**line_attrs,
),
**trace_attrs)
traces.append(hist_trace)
last_hist_year = hist_series.index.max()
forecast_series = df.loc[df.Forecast |
(df.index == last_hist_year), y_column]
else:
forecast_series = df.loc[df.Forecast, y_column]
forecast_series = forecast_series.dropna()
if len(forecast_series):
color = series.get_color(forecast=True)
if self.stacked:
trace_attrs['stackgroup'] = 'forecast'
if self.fill:
trace_attrs['fillcolor'] = color
else:
line_attrs['dash'] = 'dash'
forecast_trace = dict(type='scatter',
x=forecast_series.index.astype(str),
y=forecast_series,
name='%s (enn.)' % series.trace_name,
hovertemplate=hovertemplate,
line=dict(
color=color,
**line_attrs,
),
showlegend=False,
**trace_attrs)
traces.insert(0, forecast_trace)
return traces
def predict_geothermal_production(variables, datasets):
target_year = variables['target_year']
yearly_renovation = variables['geothermal_existing_building_renovation'] / 100
new_building_geothermal_percentage = variables['geothermal_new_building_installation_share'] / 100
old_heat_use_df = generate_heat_use_per_net_area_forecast_existing_buildings()
new_heat_use_df = generate_heat_use_per_net_area_forecast_new_buildings()
building_df = generate_building_floor_area_forecast()
hist_geo = get_historical_production()
DH_PERCENTAGE = 0.85
df = building_df.copy()
hist_df = df[~df.Forecast]
last_historical_year = hist_geo.index.max()
last_area = df.loc[last_historical_year]
dh_area = last_area.sum() * DH_PERCENTAGE
dh_left = dh_area.copy()
geo = pd.Series()
for year in range(last_historical_year + 1, target_year + 1):
dh_left *= (1 - yearly_renovation)
geo.loc[year] = float(dh_area - dh_left)
df = pd.DataFrame()
df['GeoBuildingNetAreaExisting'] = geo
s = df['GeoBuildingNetAreaExisting'] * old_heat_use_df['HeatUsePerNetArea'] / 1000000 # kWh -> GWh
df['GeoEnergyProductionExisting'] = s
bdf = building_df.loc[building_df.index >= last_historical_year]
bdf = bdf.drop(columns='Forecast').sum(axis=1)
bdf = bdf.diff().dropna()
df['GeoBuildingNetAreaNew'] = (bdf * new_building_geothermal_percentage).cumsum()
df['GeoEnergyProductionNew'] = df['GeoBuildingNetAreaNew'] * new_heat_use_df / 1000000 # kWh -> GWh
df['GeoBuildingNetAreaNew'] /= 1000000
df['GeoBuildingNetAreaExisting'] /= 1000000
ep = df['GeoEnergyProductionExisting']
ep = hist_geo.append(ep).sort_index()
start_year = find_consecutive_start(hist_geo.index)
df = df.reindex(range(start_year, df.index.max() + 1))
df['GeoEnergyProductionExisting'] = ep
df['GeoEnergyProduction'] = df['GeoEnergyProductionNew'].fillna(0) + df['GeoEnergyProductionExisting']
df['ElectricityUse'] = df['GeoEnergyProduction'] / variables['geothermal_heat_pump_cop']
edf = predict_electricity_emission_factor()
df['Emissions'] = df['ElectricityUse'] * edf['EmissionFactor'] / 1000
dhdf, _ = calc_district_heating_unit_emissions_forecast()
df['EmissionReductions'] = df['GeoEnergyProduction'] * dhdf['Emission factor'] / 1000
df['NetEmissions'] = df['Emissions'] - df['EmissionReductions']
production_per_hole = get_borehole_production(variables['geothermal_borehole_depth'])
boreholes = df['GeoEnergyProduction'] * 1000000 / production_per_hole
boreholes[last_historical_year] = 0
boreholes = boreholes.sort_index()
df['BoreholesPerYear'] = boreholes.diff().fillna(0)
# assume grid formation
df['BoreholeAreaNeeded'] = (boreholes * 25**2) / 1000000 # m2 -> km2
df['Forecast'] = False
df.loc[df.index > last_historical_year, 'Forecast'] = True
return df