#%matplotlib inline
import pandas as pd
import numpy as np
jrc_base_dir = "data/jrc-idees-2015"
eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
# import EU ratios df as csv
df=pd.read_csv('resources/industry_sector_ratios.csv', sep=';', index_col=0)
sub_sheet_name_dict = { 'Iron and steel':'ISI',
'Chemicals Industry':'CHI',
'Non-metallic mineral products': 'NMM',
'Pulp, paper and printing': 'PPA',
'Food, beverages and tobacco': 'FBT',
'Non Ferrous Metals' : 'NFM',
'Transport Equipment': 'TRE',
'Machinery Equipment': 'MAE',
'Textiles and leather':'TEL',
'Wood and wood products': 'WWP',
'Other Industrial Sectors': 'OIS'}
index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emission']
countries_df = pd.DataFrame(columns=index) #data frame final energy consumption per country and source
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
rename = {"GR" : "EL",
"GB" : "UK"}
eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI', 'CZ',
'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV',
'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI'] + ['CY','MT']
countries = non_EU + [rename.get(eu,eu) for eu in eu28[:-2]]
sectors = ['Iron and steel','Chemicals Industry','Non-metallic mineral products',
'Pulp, paper and printing', 'Food, beverages and tobacco', 'Non Ferrous Metals',
'Transport Equipment', 'Machinery Equipment', 'Textiles and leather',
'Wood and wood products', 'Other Industrial Sectors']
sect2sub = {'Iron and steel':['Electric arc','Integrated steelworks'],
'Chemicals Industry': ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.'],
'Non-metallic mineral products': ['Cement','Ceramics & other NMM','Glass production'],
'Pulp, paper and printing': ['Pulp production','Paper production','Printing and media reproduction'],
'Food, beverages and tobacco': ['Food, beverages and tobacco'],
'Non Ferrous Metals': ['Alumina production', 'Aluminium - primary production', 'Aluminium - secondary production', 'Other non-ferrous metals'],
'Transport Equipment': ['Transport Equipment'],
'Machinery Equipment': ['Machinery Equipment'],
'Textiles and leather': ['Textiles and leather'],
'Wood and wood products' :['Wood and wood products'],
'Other Industrial Sectors':['Other Industrial Sectors']}
out_dic ={'Electric arc': 'Electric arc',
'Integrated steelworks': 'Integrated steelworks',
'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
'Other chemicals':'Other chemicals (kt ethylene eq.)',
'Pharmaceutical products etc.':'Pharmaceutical products etc. (kt ethylene eq.)',
'Cement':'Cement (kt)',
'Ceramics & other NMM':'Ceramics & other NMM (kt bricks eq.)',
'Glass production':'Glass production (kt)',
'Pulp production':'Pulp production (kt)',
'Paper production':'Paper production (kt)',
'Printing and media reproduction':'Printing and media reproduction (kt paper eq.)',
'Food, beverages and tobacco': 'Physical output (index)',
'Alumina production':'Alumina production (kt)',
'Aluminium - primary production': 'Aluminium - primary production',
'Aluminium - secondary production': 'Aluminium - secondary production',
'Other non-ferrous metals' : 'Other non-ferrous metals (kt lead eq.)',
'Transport Equipment': 'Physical output (index)',
'Machinery Equipment': 'Physical output (index)',
'Textiles and leather': 'Physical output (index)',
'Wood and wood products': 'Physical output (index)',
'Other Industrial Sectors': 'Physical output (index)'}
loc_dic={'Iron and steel':[5,8],
'Chemicals Industry': [7,11],
'Non-metallic mineral products': [6,10],
'Pulp, paper and printing': [7,11],
'Food, beverages and tobacco': [2,6],
'Non Ferrous Metals': [9,14],
'Transport Equipment': [3,5],
'Machinery Equipment': [3,5],
'Textiles and leather': [3,5],
'Wood and wood products': [3,5],
'Other Industrial Sectors': [3,5]}
# In the summary sheet (IDEES database) some names include a white space
dic_sec_summary = {'Iron and steel': 'Iron and steel',
'Chemicals Industry': 'Chemicals Industry',
'Non-metallic mineral products': 'Non-metallic mineral products',
'Pulp, paper and printing': 'Pulp, paper and printing',
'Food, beverages and tobacco': ' Food, beverages and tobacco',
'Non Ferrous Metals': 'Non Ferrous Metals',
'Transport Equipment': ' Transport Equipment',
'Machinery Equipment': ' Machinery Equipment',
'Textiles and leather': ' Textiles and leather',
'Wood and wood products': ' Wood and wood products',
'Other Industrial Sectors': ' Other Industrial Sectors'}
#countries=['CH']
dic_countries={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
'UA':'Ukraine', 'TR':'Turkey', }
dic_sec ={'Iron and steel':'Iron & steel industry',
'Chemicals Industry': 'Chemical and Petrochemical industry',
'Non-metallic mineral products': 'Non-ferrous metal industry',
'Pulp, paper and printing': 'Paper, Pulp and Print',
'Food, beverages and tobacco': 'Food and Tabacco',
'Non Ferrous Metals': 'Non-metallic Minerals (Glass, pottery & building mat. Industry)',
'Transport Equipment': 'Transport Equipment',
'Machinery Equipment': 'Machinery',
'Textiles and leather': 'Textile and Leather',
'Wood and wood products': 'Wood and Wood Products',
'Other Industrial Sectors': 'Non-specified (Industry)'}
# Mining and Quarrying, Construction
#Annual energy consumption in Switzerland by sector in 2015 (in TJ)
#From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
#http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
dic_Switzerland ={'Iron and steel': 7889.,
'Chemicals Industry': 26871.,
'Non-metallic mineral products': 15513.+3820.,
'Pulp, paper and printing': 12004.,
'Food, beverages and tobacco': 17728.,
'Non Ferrous Metals': 3037.,
'Transport Equipment': 14993.,
'Machinery Equipment': 4724.,
'Textiles and leather': 1742.,
'Wood and wood products': 0.,
'Other Industrial Sectors': 10825.,
'current electricity': 53760.}
dic_sec_position={}
for country in countries:
countries_df.loc[country] = 0
print (country)
for sector in sectors:
if country in non_EU:
if country == 'CH':
e_country = dic_Switzerland[sector]*tj_to_ktoe
else:
# estimate physical output
#energy consumption in the sector and country
excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]),
sheet_name='2016', index_col=2,header=0, skiprows=1 ,squeeze=True)
e_country = excel_balances.loc[dic_sec[sector], 'Total all products']
#energy consumption in the sector and EU28
excel_sum_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_EU28.xlsx'.format(jrc_base_dir),
sheet_name='Ind_Summary', index_col=0,header=0,squeeze=True) # the summary sheet
s_sum_out = excel_sum_out.iloc[49:76,-1]
e_EU28 = s_sum_out[dic_sec_summary[sector]]
ratio_country_EU28=e_country/e_EU28
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_EU28.xlsx'.format(jrc_base_dir),
sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
for subsector in sect2sub[sector]:
output = ratio_country_EU28*s_out[out_dic[subsector]]
for ind in index:
countries_df.loc[country, ind] += float(output*df.loc[ind, subsector]) # kton * MWh = GWh (# kton * tCO2 = ktCO2)
else:
# read the input sheets
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,country), sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
for subsector in sect2sub[sector]:
output = s_out[out_dic[subsector]]
for ind in index:
countries_df.loc[country, ind] += output*df.loc[ind, subsector] #kton * MWh = GWh (# kton * tCO2 = ktCO2)
countries_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2)
# save current electricity consumption
for country in countries:
if country in non_EU:
if country == 'CH':
countries_df.loc[country, 'current electricity']=dic_Switzerland['current electricity']*tj_to_ktoe*ktoe_to_twh
else:
excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]),
sheet_name='2016', index_col=1,header=0, skiprows=1 ,squeeze=True)
countries_df.loc[country, 'current electricity'] = excel_balances.loc['Industry', 'Electricity']*ktoe_to_twh
else:
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,country),
sheet_name='Ind_Summary',index_col=0,header=0,squeeze=True) # the summary sheet
s_out = excel_out.iloc[27:48,-1]
countries_df.loc[country, 'current electricity'] = s_out['Electricity']*ktoe_to_twh
print(countries_df.loc[country, 'current electricity'])
# save df as csv
for ind in index:
countries_df[ind]=countries_df[ind].astype('float')
countries_df = countries_df.round(3)
countries_df.rename(index={value : key for key,value in rename.items()},inplace=True)
rename_sectors = {'elec':'electricity',
'biomass':'solid biomass',
'heat':'low-temperature heat'}
countries_df.rename(columns=rename_sectors,inplace=True)
countries_df.to_csv('resources/industrial_demand_per_country.csv',
float_format='%.2f')