build_heat_totals: use more years for regression
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@ -15,17 +15,22 @@ idx = pd.IndexSlice
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def approximate_heat_demand(energy_totals, hdd):
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if isinstance(hdd, str):
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hdd = pd.read_csv(hdd, index_col=0).T
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hdd.index = hdd.index.astype(int)
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countries = hdd.columns
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demands = {}
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for kind, sector in product(["total", "electricity"], ["services", "residential"]):
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row = idx[:, 2007:2015]
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# reduced number years (2007-2021) for regression because it implicitly
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# assumes a constant building stock
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row = idx[:, 2007:2021]
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col = f"{kind} {sector} space"
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demand = energy_totals.loc[row, col].unstack(0)
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# ffill for GB in 2020- and bfill for CH 2007-2009
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# compromise to have more years available for the fit
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demand = demand.ffill(axis=0).bfill(axis=0)
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demand_approx = {}
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for c in countries:
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@ -62,16 +67,15 @@ def approximate_heat_demand(energy_totals, hdd):
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from helper import mock_snakemake
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("build_energy_totals")
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snakemake = mock_snakemake("build_heat_totals")
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hdd = pd.read_csv(snakemake.input.hdd, index_col=0).T
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hdd.index = hdd.index.astype(int)
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energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=[0, 1])
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countries = hdd.columns
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heat_demand = approximate_heat_demand(energy_totals, hdd)
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heat_demand.to_csv(snakemake.output.heat_totals)
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