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energy_totals: combined energy totals for all years

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Fabian Neumann 2022-07-23 17:05:07 +02:00
parent eee6651d12
commit ba8b3f9ce7
2 changed files with 54 additions and 34 deletions
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Snakefile
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@ -243,6 +243,7 @@ rule build_energy_totals:
district_heat_share='data/district_heat_share.csv', district_heat_share='data/district_heat_share.csv',
eurostat=directory("data/eurostat-energy_balances-june_2021_edition"), eurostat=directory("data/eurostat-energy_balances-june_2021_edition"),
output: output:
energy_name_full='resources/energy_totals_full.csv',
energy_name='resources/energy_totals.csv', energy_name='resources/energy_totals.csv',
co2_name='resources/co2_totals.csv', co2_name='resources/co2_totals.csv',
transport_name='resources/transport_data.csv' transport_name='resources/transport_data.csv'

87
scripts/build_energy_totals.py
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@ -125,21 +125,25 @@ def build_eurostat(countries, year=None):
# convert ktoe/a to TWh/a # convert ktoe/a to TWh/a
df *= 11.63 / 1e3 df *= 11.63 / 1e3
df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)
if year: if year:
df = df.xs(str(year), level='year') df = df.xs(year, level='year')
return df return df
def build_swiss(year=None): def build_swiss(year=None):
"""Return a pd.Series of Swiss energy data in TWh/a""" """Return a pd.DataFrame of Swiss energy data in TWh/a"""
fn = snakemake.input.swiss fn = snakemake.input.swiss
df = pd.read_csv(fn, index_col=[0,1]).loc["CH"] df = pd.read_csv(fn, index_col=[0,1]).stack().unstack('item')
df.index.names = ["country", "year"]
df.index = df.index.set_levels(df.index.levels[1].astype(int), level=1)
if year: if year:
df = df[str(year)] df = df.xs(year, level='year')
# convert PJ/a to TWh/a # convert PJ/a to TWh/a
df /= 3.6 df /= 3.6
@ -373,6 +377,8 @@ def build_idees(countries, year=None):
if year: if year:
df = df.xs(int(year), level='year') df = df.xs(int(year), level='year')
df.columns.name = 'item'
return df return df
@ -391,15 +397,21 @@ def build_energy_totals(countries, eurostat, swiss, idees):
) )
to_drop = ["passenger cars", "passenger car efficiency"] to_drop = ["passenger cars", "passenger car efficiency"]
df = idees.reindex(countries).drop(to_drop, axis=1)
new_index = pd.MultiIndex.from_product(
[countries, eurostat.index.levels[1]],
names=["country", "year"]
)
df = idees.reindex(new_index).drop(to_drop, axis=1)
eurostat_countries = eurostat.index.levels[0] eurostat_countries = eurostat.index.levels[0]
in_eurostat = df.index.intersection(eurostat_countries) in_eurostat = df.index.levels[0].intersection(eurostat_countries)
# add international navigation # add international navigation
slicer = idx[in_eurostat, :, "International maritime bunkers", :] slicer = idx[in_eurostat, :, :, "International maritime bunkers", :]
fill_values = eurostat.loc[slicer, "Total"].groupby(level=0).sum() fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
df.loc[in_eurostat, "total international navigation"] = fill_values df.loc[in_eurostat, "total international navigation"] = fill_values
# add swiss energy data # add swiss energy data
@ -409,17 +421,20 @@ def build_energy_totals(countries, eurostat, swiss, idees):
# get values for missing countries based on Eurostat EnergyBalances # get values for missing countries based on Eurostat EnergyBalances
# divide cooking/space/water according to averages in EU28 # divide cooking/space/water according to averages in EU28
missing = df.index[df["total residential"].isna()] to_fill = df.index[df["total residential"].isna() & df.index.get_level_values('country').isin(eurostat_countries)]
to_fill = missing.intersection(eurostat_countries)
uses = ["space", "cooking", "water"] uses = ["space", "cooking", "water"]
c = to_fill.get_level_values('country')
y = to_fill.get_level_values('year')
for sector in ["residential", "services", "road", "rail"]: for sector in ["residential", "services", "road", "rail"]:
# fuel use # fuel use
for fuel in ["electricity", "total"]: for fuel in ["electricity", "total"]:
slicer = idx[to_fill, :, :, eurostat_sectors[sector]]
fill_values = eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum() slicer = idx[c, y, :, :, eurostat_sectors[sector]]
fill_values = eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0,1]).sum()
df.loc[to_fill, f"{fuel} {sector}"] = fill_values df.loc[to_fill, f"{fuel} {sector}"] = fill_values
for sector in ["residential", "services"]: for sector in ["residential", "services"]:
@ -462,28 +477,28 @@ def build_energy_totals(countries, eurostat, swiss, idees):
nonelectric_use = no_norway[f"total {sector} {use}"] - no_norway[f"electricity {sector} {use}"] nonelectric_use = no_norway[f"total {sector} {use}"] - no_norway[f"electricity {sector} {use}"]
nonelectric = no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"] nonelectric = no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
fraction = nonelectric_use.div(nonelectric).mean() fraction = nonelectric_use.div(nonelectric).mean()
df.loc["NO", f"total {sector} {use}"] = total_heating * fraction df.loc["NO", f"total {sector} {use}"] = (total_heating * fraction).values
df.loc["NO", f"electricity {sector} {use}"] = total_heating * fraction * elec_fraction df.loc["NO", f"electricity {sector} {use}"] = (total_heating * fraction * elec_fraction).values
# Missing aviation # Missing aviation
slicer = idx[to_fill, :, :, "Domestic aviation"] slicer = idx[c, y, :, :, "Domestic aviation"]
fill_values = eurostat.loc[slicer, "Total"].groupby(level=0).sum() fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
df.loc[to_fill, "total domestic aviation"] = fill_values df.loc[to_fill, "total domestic aviation"] = fill_values
slicer = idx[to_fill, :, "International aviation", :] slicer = idx[c, y, :, "International aviation", :]
fill_values = eurostat.loc[slicer, "Total"].groupby(level=0).sum() fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
df.loc[to_fill, "total international aviation"] = fill_values df.loc[to_fill, "total international aviation"] = fill_values
# missing domestic navigation # missing domestic navigation
slicer = idx[to_fill, :, :, "Domestic navigation"] slicer = idx[c, y, :, :, "Domestic navigation"]
fill_values = eurostat.loc[slicer, "Total"].groupby(level=0).sum() fill_values = eurostat.loc[slicer, "Total"].groupby(level=[0,1]).sum()
df.loc[to_fill, "total domestic navigation"] = fill_values df.loc[to_fill, "total domestic navigation"] = fill_values
# split road traffic for non-IDEES # split road traffic for non-IDEES
missing = df.index[df["total passenger cars"].isna()] missing = df.index[df["total passenger cars"].isna()]
for fuel in ["total", "electricity"]: for fuel in ["electricity", "total"]:
selection = [ selection = [
f"{fuel} passenger cars", f"{fuel} passenger cars",
f"{fuel} other road passenger", f"{fuel} other road passenger",
@ -525,19 +540,20 @@ def build_energy_totals(countries, eurostat, swiss, idees):
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1) df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1)
if "BA" in df.index: if "BA" in df.index:
# fill missing data for BA (services and road energy data) # fill missing data for BA proportional to RS
# proportional to RS with ratio of total residential demand ratio = (df.loc["BA"].loc[2014:2020] / df.loc["RS"].loc[2014:2020]).mean()
missing = df.loc["BA"] == 0.0 df.loc["BA"] = (ratio * df.loc["RS"]).values
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
df.loc['BA', missing] = ratio * df.loc["RS", missing]
# Missing district heating share # Missing district heating share
dh_share = pd.read_csv(snakemake.input.district_heat_share, dh_share = pd.read_csv(snakemake.input.district_heat_share,
index_col=0, usecols=[0, 1]) index_col=0, usecols=[0, 1])
dh_share = pd.concat({y: dh_share for y in range(1990, 2021)}, names=["year", "country"]).swaplevel()
dh_share = dh_share.div(100).reindex(df.index)
# make conservative assumption and take minimum from both data sets # make conservative assumption and take minimum from both data sets
df["district heat share"] = (pd.concat([df["district heat share"], item = "district heat share"
dh_share.reindex(index=df.index)/100], df[item] = pd.concat([dh_share, df[item]], axis=1).min(axis=1)
axis=1).min(axis=1))
return df return df
@ -673,6 +689,7 @@ if __name__ == "__main__":
snakemake = mock_snakemake('build_energy_totals') snakemake = mock_snakemake('build_energy_totals')
config = snakemake.config["energy"] config = snakemake.config["energy"]
data_year = int(config["energy_totals_year"])
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index") nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
population = nuts3["pop"].groupby(nuts3.country).sum() population = nuts3["pop"].groupby(nuts3.country).sum()
@ -680,12 +697,14 @@ if __name__ == "__main__":
countries = population.index countries = population.index
idees_countries = countries.intersection(eu28) idees_countries = countries.intersection(eu28)
data_year = config["energy_totals_year"] eurostat = build_eurostat(countries.difference(['CH']))
eurostat = build_eurostat(countries.difference(['CH']), data_year) swiss = build_swiss()
swiss = build_swiss(data_year) idees = build_idees(idees_countries)
idees = build_idees(idees_countries, data_year)
energy = build_energy_totals(countries, eurostat, swiss, idees) energy = build_energy_totals(countries, eurostat, swiss, idees)
energy.to_csv(snakemake.output.energy_name_full)
energy = energy.xs(data_year, level='year')
energy.to_csv(snakemake.output.energy_name) energy.to_csv(snakemake.output.energy_name)
base_year_emissions = config["base_emissions_year"] base_year_emissions = config["base_emissions_year"]