108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
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import pandas as pd
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# sub-sectors as used in PyPSA-Eur-Sec and listed in JRC-IDEES industry sheets
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sub_sectors = {'Iron and steel' : ['Integrated steelworks','Electric arc'],
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'Non-ferrous metals' : ['Alumina production','Aluminium - primary production','Aluminium - secondary production','Other non-ferrous metals'],
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'Chemicals' : ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.', 'Basic chemicals feedstock'],
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'Non-metalic mineral' : ['Cement','Ceramics & other NMM','Glass production'],
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'Printing' : ['Pulp production','Paper production','Printing and media reproduction'],
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'Food' : ['Food, beverages and tobacco'],
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'Transport equipment' : ['Transport Equipment'],
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'Machinery equipment' : ['Machinery Equipment'],
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'Textiles and leather' : ['Textiles and leather'],
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'Wood and wood products' : ['Wood and wood products'],
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'Other Industrial Sectors' : ['Other Industrial Sectors'],
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}
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# name in JRC-IDEES Energy Balances
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eb_sheet_name = {'Integrated steelworks' : 'cisb',
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'Electric arc' : 'cise',
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'Alumina production' : 'cnfa',
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'Aluminium - primary production' : 'cnfp',
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'Aluminium - secondary production' : 'cnfs',
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'Other non-ferrous metals' : 'cnfo',
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'Basic chemicals' : 'cbch',
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'Other chemicals' : 'coch',
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'Pharmaceutical products etc.' : 'cpha',
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'Basic chemicals feedstock' : 'cpch',
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'Cement' : 'ccem',
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'Ceramics & other NMM' : 'ccer',
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'Glass production' : 'cgla',
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'Pulp production' : 'cpul',
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'Paper production' : 'cpap',
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'Printing and media reproduction' : 'cprp',
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'Food, beverages and tobacco' : 'cfbt',
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'Transport Equipment' : 'ctre',
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'Machinery Equipment' : 'cmae',
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'Textiles and leather' : 'ctel',
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'Wood and wood products' : 'cwwp',
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'Mining and quarrying' : 'cmiq',
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'Construction' : 'ccon',
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'Non-specified': 'cnsi',
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}
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fuels = {'all' : ['All Products'],
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'solid' : ['Solid Fuels'],
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'liquid' : ['Total petroleum products (without biofuels)'],
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'gas' : ['Gases'],
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'heat' : ['Nuclear heat','Derived heat'],
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'biomass' : ['Biomass and Renewable wastes'],
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'waste' : ['Wastes (non-renewable)'],
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'electricity' : ['Electricity'],
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}
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ktoe_to_twh = 0.011630
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
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jrc_names = {"GR" : "EL",
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"GB" : "UK"}
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year = 2015
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summaries = {}
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#for some reason the Energy Balances list Other Industrial Sectors separately
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ois_subs = ['Mining and quarrying','Construction','Non-specified']
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for ct in eu28:
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print(ct)
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filename = 'data/jrc-idees-2015/JRC-IDEES-2015_EnergyBalance_{}.xlsx'.format(jrc_names.get(ct,ct))
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summary = pd.DataFrame(index=list(fuels.keys()) + ['other'])
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for sector in sub_sectors:
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if sector == 'Other Industrial Sectors':
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subs = ois_subs
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else:
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subs = sub_sectors[sector]
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for sub in subs:
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df = pd.read_excel(filename,
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sheet_name=eb_sheet_name[sub],
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index_col=0)
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s = df[year].astype(float)
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for fuel in fuels:
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summary.at[fuel,sub] = s[fuels[fuel]].sum()
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summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index^['all','other'],sub].sum()
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summary['Other Industrial Sectors'] = summary[ois_subs].sum(axis=1)
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summary.drop(columns=ois_subs,inplace=True)
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summaries[ct] = summary*ktoe_to_twh
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final_summary = pd.concat(summaries,axis=1)
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final_summary.index.name = 'TWh/a'
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final_summary.to_csv(snakemake.output.industrial_energy_demand_per_country_today)
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