64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
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"""Approximate heat demand for all weather years."""
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import pandas as pd
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from itertools import product
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from numpy.polynomial import Polynomial
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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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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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col = f"{kind} {sector} space"
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demand = energy_totals.loc[row, col].unstack(0)
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demand_approx = {}
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for c in countries:
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Y = demand[c].dropna()
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X = hdd.loc[Y.index, c]
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to_predict = hdd.index.difference(Y.index)
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X_pred = hdd.loc[to_predict, c]
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p = Polynomial.fit(X, Y, 1)
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Y_pred = p(X_pred)
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demand_approx[c] = pd.Series(Y_pred, index=to_predict)
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demand_approx = pd.DataFrame(demand_approx)
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demand_approx = pd.concat([demand, demand_approx]).sort_index()
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demands[f"{kind} {sector} space"] = demand_approx.groupby(demand_approx.index).sum()
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demands = pd.concat(demands).unstack().T.clip(lower=0)
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demands.index.names = ["country", "year"]
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return demands
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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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snakemake = mock_snakemake('build_energy_totals')
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hdd = pd.read_csv(snakemake.input.hdd, index_col=0).T
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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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