def fig_scaled_electricity_profile():
"""
Comparison of different methods to fetch the annual electricity demand
to scale the entso-e profile.
"""
ax = plt.figure(figsize=(8, 4)).add_subplot(1, 1, 1)
fs = geometries.get_federal_states_polygon()
p = pd.Series()
p1 = demand_elec.get_entsoe_profile_by_region(fs, 2014, "test", "entsoe")
p["entsoe"] = p1.sum().sum()
p2 = demand_elec.get_entsoe_profile_by_region(fs, 2013, "test", "bmwi")
p["bmwi"] = p2.sum().sum()
p3 = demand_elec.get_entsoe_profile_by_region(fs, 2013, "test", "openego")
p["openego"] = p3.sum().sum()
p4 = demand_elec.get_entsoe_profile_by_region(fs, 2011, "test", 555555)
p["user value"] = p4.sum().sum()
p.plot(kind="bar", ax=ax)
plt.xticks(rotation=0)
ax.set_ylabel("energy demand [GWh]")
plt.title("Energy demand of Germany to scale the overall demand.")
plt.subplots_adjust(right=0.95, left=0.13, bottom=0.13, top=0.91)
return "scaled_electricity_profile"
def test_profile_by_region_with_wrong_annual_values(self):
msg = (
"200 of type <class 'str'> is not a valid input for "
"'annual_demand'"
)
with assert_raises_regexp(ValueError, msg):
demand_elec.get_entsoe_profile_by_region(
self.geo, 2011, "test", "200"
)
def scenario_elec_demand(table, regions, year, name, weather_year=None):
"""
Parameters
----------
table
regions
year
name
weather_year
Returns
-------
"""
if weather_year is None:
demand_year = year
else:
demand_year = weather_year
df = demand_elec.get_entsoe_profile_by_region(regions,
demand_year,
name,
annual_demand="bmwi")
df = pd.concat([df], axis=1, keys=["electrical_load"]).swaplevel(0, 1, 1)
df = df.reset_index(drop=True)
if not calendar.isleap(year) and len(df) > 8760:
df = df.iloc[:8760]
return pd.concat([table, df], axis=1).sort_index(1)
def fig_electricity_profile_from_entsoe():
"""
Electricity profile from entso-e scaled on the annual demand of three
different federal states.
"""
ax = plt.figure(figsize=(10, 4)).add_subplot(1, 1, 1)
fs = geometries.get_federal_states_polygon()
df = demand_elec.get_entsoe_profile_by_region(fs, 2014, "federal_states",
"bmwi")
df[["NW", "NI", "MV"]].mul(1000).plot(ax=ax)
plt.title("Demand profile for three federal states in 2014")
ax.set_ylabel("electricity demand [GW]")
ax.set_xlabel("hour of the year")
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc="upper left", bbox_to_anchor=(1, 0.5))
plt.subplots_adjust(right=0.91, left=0.08, bottom=0.13, top=0.91)
return "electricity_profile_from_entsoe"
def test_profile_by_region_with_user_annual_values(self):
d4 = demand_elec.get_entsoe_profile_by_region(
self.geo, 2011, "test", 200
)
eq_(int(round(d4.sum().sum(), 0)), 200)
def test_profile_by_region_with_openego_annual_values(self):
d3 = demand_elec.get_entsoe_profile_by_region(
self.geo, 2013, "test", "openego"
)
eq_(int(d3.sum().sum()), 31726254)
def test_profile_by_region_with_bmwi_annual_values(self):
d2 = demand_elec.get_entsoe_profile_by_region(
self.geo, 2013, "test", "bmwi"
)
eq_(int(d2.sum().sum()), 535684999)
def test_profile_by_region_with_entsoe_annual_values(self):
d1 = demand_elec.get_entsoe_profile_by_region(
self.geo, 2014, "test", "entsoe"
)
eq_(int(d1.sum().sum()), 519752444)
def test_profile_by_region_with_entsoe_annual_values(self):
d1 = demand_elec.get_entsoe_profile_by_region(self.geo, 2016, "test",
"entsoe")
eq_(round(d1.sum().sum() / 1e6, 1), 487.0) # TWh
