# Automatically reload the file if it is modified:
%load_ext autoreload
%autoreload 2
# Load the macro parameters and necessary packages:
%run ./nW_BE_demand_model_macro.ipynb
# Option to print and plot the results:
post_process = True

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

# Inputs - Define Sufficiency Scenario Data (SUF)
ref_RS_sur_tot = 625235.722685535*1e+3
ref_RS_sur_spe = ref_RS_sur_tot/population_dict[2019] # [m²/person]
ref_RS_sur_hld = ref_RS_sur_tot/households_dict[2019] # [m²/household]

# Inputs - Define Sufficiency Scenario Data (SUF)
pro_RS_sur_spe = -0.10
# Outputs - Sufficiency Scenario Data (SUF)
SUF_data = {"RS specific surface [m²/person]": linear_growth(2019, ref_RS_sur_spe, 
                                                             2050, ref_RS_sur_spe*(1+pro_RS_sur_spe), years)}
df_SUF = pd.DataFrame(SUF_data, index=years)
df_SUF["population [person]"]                 = df_SUF.index.map(population_dict)
df_SUF["households [household]"]              = df_SUF.index.map(households_dict)
df_SUF["households [person]"]                 = df_SUF["population [person]"]            /df_SUF["households [household]"]
df_SUF["RS total surface [Mm²]"]              = df_SUF["RS specific surface [m²/person]"]*df_SUF["population [person]"]*1e-6
df_SUF["RS household surface [m²/household]"] = df_SUF["RS total surface [Mm²]"]*1e+6    /df_SUF["households [household]"]

# Inputs - Define Sufficiency Scenario Data (SUF)
ref_TS_sur_tot = 227073.149760375*1e+3
ref_TS_sur_spe = ref_TS_sur_tot/population_dict[2019] # [m²/person]

# Inputs - Define Sufficiency Scenario Data (SUF)
pro_TS_sur_spe = -0.10
# Outputs - Sufficiency Scenario Data (SUF)
df_SUF["TS specific surface [m²/person]"] = linear_growth(2019, ref_TS_sur_spe, 
                                                          2050, ref_TS_sur_spe*(1+pro_TS_sur_spe), years)
df_SUF["TS total surface [Mm²]"] = df_SUF["TS specific surface [m²/person]"]*df_SUF["population [person]"]*1e-6

# Processing - Sectors
df_BD_macro = {
    'Demographic':   df_SUF[["population [person]","households [household]","households [person]"]].T,
    'Residential':   df_SUF[["RS total surface [Mm²]","RS household surface [m²/household]","RS specific surface [m²/person]"]].T,
    'Tertiary':      df_SUF[["TS total surface [Mm²]","TS specific surface [m²/person]"]].T,
}
rows = []
for sector, df in df_BD_macro.items():
    temp = df.copy()
    temp['Sector'] = sector
    temp['Parameter'] = temp.index
    temp = temp.reset_index(drop=True)
    rows.append(temp)
df_BD_macro = pd.concat(rows, ignore_index=True)

if post_process:
    # === Full table ===
    df_fec = df_BD_macro
    df_fec['Sector'] = df_fec['Sector'].mask(df_fec['Sector'].duplicated(), '')
    df_fec_r = df_fec[['Sector', 'Parameter'] + list(years)]
    styled = (
        df_fec_r.style
          .hide(axis='index')
          .set_table_attributes('style="width:100%;table-layout:fixed;"')
          .apply(highlight_mode_separator, axis=1)
          .apply(lambda row: [bold_mode(cell, row['Sector'], col) for col, cell in zip(df_fec_r.columns, row)], axis=1)
          .set_properties(subset=['Parameter'], **{'font-style':'italic'})
          .format({year: "{:.3f}" for year in years})
    )
    display(styled)

    # === Bar chart ===
    plot_data = df_BD_macro.set_index('Parameter')[years].T
    plot_data = plot_data[['RS specific surface [m²/person]', 'TS specific surface [m²/person]']]
    ax = plot_data.plot(
        kind='barh',
        figsize=(14, 5), 
        width=0.8,
        edgecolor='white'
        )
    ax.set_title('Projections of surface area per sector', fontsize=11, pad=15)
    ax.set_xlabel('Specific surface area [m²/person]', fontsize=10, ha='right', va='top')
    ax.set_ylabel('Years', fontsize=10, rotation=0, ha='left', va='bottom') 
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Sector", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    plt.tight_layout()
    plt.show()

# Inputs - Repartition of the thermal energy services (TES) in residential buildings for the reference year (2019)
ref_RS_tes_sht = 3664.049*ktoe_to_GWh*1e+6/ref_RS_sur_tot # [kWh/m²]
ref_RS_tes_scl =   23.455*ktoe_to_GWh*1e+6/ref_RS_sur_tot # [kWh/m²]
ref_RS_tes_shw =  663.435*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_RS_tes_cok =   96.395*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
if post_process:
    print(f'Energy service for space heating: {round(ref_RS_tes_sht,3)} kWh/m²')
    print(f'Energy service for space cooling:  {round(ref_RS_tes_scl,3)} kWh/m²')
    print(f'Energy service for hot water:    {round(ref_RS_tes_shw,3)} kWh/person')
    print(f'Energy service for cooking:      {round(ref_RS_tes_cok,3)} kWh/household')

Energy service for space heating: 68.155 kWh/m²
Energy service for space cooling:  0.436 kWh/m²
Energy service for hot water:    674.961 kWh/person
Energy service for cooking:      226.553 kWh/household

acc_RS_tes_sht_ren = 2
cur_RS_tes_sht_ren = -0.458 # kWh/m²/year
trg_RS_tes_sht     = ref_RS_tes_sht + acc_RS_tes_sht_ren*cur_RS_tes_sht_ren*(2050-2019) # [kWh/m²]

# --- The same assumption, read as renovation activity -------------------------
# The line above moves the stock average without saying how many dwellings are
# touched or how deeply. Renovation is what delivers it, so the link is written
# down here. If a constant share `rat` of the stock is renovated every year and
# each renovation cuts that dwelling's heating need by a fraction `dep`, the
# stock average falls *linearly*, which is the shape assumed above:
#     I(t) = I(0) * [1 - dep*rat*t]                      for rat*t <= 1
# hence dep*rat = -acc*cur/ref, and fixing either one fixes the other. ONE new
# assumption enters -- the depth -- and the rate follows from the trajectory, so
# trg_RS_tes_sht and everything downstream of it are numerically unchanged.
#
# The depth is set at the threshold the European Commission uses to call a
# renovation "deep" (Recommendation (EU) 2019/786): the scenario then reads as
# "every energy renovation from now on is a deep one".
dep_RS_tes_sht_ren = 0.60                                                    # [-]      cut in heating need per renovation
shr_RS_tes_sht_ren = (1 - trg_RS_tes_sht/ref_RS_tes_sht)/dep_RS_tes_sht_ren  # [-]      share of the stock renovated by 2050
rat_RS_tes_sht_ren = shr_RS_tes_sht_ren/(2050-2019)                          # [1/year] renovation rate it implies
assert 0 < shr_RS_tes_sht_ren <= 1, (
    f"a renovation depth of {dep_RS_tes_sht_ren:.0%} is too shallow for this "
    f"trajectory: it would need {shr_RS_tes_sht_ren:.0%} of the stock renovated "
    f"by 2050. Deepen the renovations, or slow the intensity target.")

# The same reading applied to the observed period, which is what sizes the choice
# of depth above. The JRC-IDEES floor-area series decomposes into a renovation
# component and net new construction (nW_BE_demand_data_aux.ipynb, cell 17); the
# observed improvement is the -0.458 kWh/m²/year fitted above, on a trend that
# starts at 79.5 kWh/m² in 2000. Attributing all of it to renovation is an upper
# bound -- new construction lowers the stock average too -- so the depth below is
# a ceiling on what the average Belgian renovation has been achieving.
obs_RS_tes_sht_2000 = 79.48    # [kWh/m²] fitted 2000 value of the -0.458 trend
obs_RS_sht_rat_ren  = 2.261    # [%/year] renovated floor area (a JRC-IDEES constant, sd 0.008)
obs_RS_sht_rat_new  = 0.746    # [%/year] net new construction (observed, sd 0.314)
obs_RS_sht_rat_all  = 3.007    # [%/year] the two together: the "3%" of the prose above
obs_RS_tes_sht_dep  = -cur_RS_tes_sht_ren/(obs_RS_sht_rat_ren/100*obs_RS_tes_sht_2000)  # [-]
assert abs(obs_RS_sht_rat_ren + obs_RS_sht_rat_new - obs_RS_sht_rat_all) < 0.01

if post_process:
    print( 'Renovation reading of the efficiency assumption:')
    print(f' -> depth assumed for each renovation:  {dep_RS_tes_sht_ren:>6.0%} of the heating need')
    print(f' -> stock renovated at least once:      {shr_RS_tes_sht_ren:>6.1%} by 2050')
    print(f' -> renovation rate implied:            {rat_RS_tes_sht_ren*100:>6.2f} %/year')
    print(f' -> observed 2000-2023, for comparison: {obs_RS_tes_sht_dep:>6.1%} deep at {obs_RS_sht_rat_ren:.3f} %/year')

d_cons_temp = 0.07
d_temp = 2
suf_RS_tes_sht = 1-d_temp*d_cons_temp
if post_process:
    print(f'Evolution of space heating energy demand: {round(-(1-(trg_RS_tes_sht*suf_RS_tes_sht)/(ref_RS_tes_sht))*100,1)}% [kWh/m²]')
    print(f' -> By 2019, the annual heating energy demand is {round(ref_RS_tes_sht,               2)} kWh/m²')
    print(f' -> By 2050, the annual heating energy demand is {round(trg_RS_tes_sht*suf_RS_tes_sht,2)} kWh/m²')

Evolution of space heating energy demand: -49.8% [kWh/m²]
 -> By 2019, the annual heating energy demand is 68.15 kWh/m²
 -> By 2050, the annual heating energy demand is 34.19 kWh/m²

acc_RS_tes_scl_ren = 1
cur_RS_tes_scl_ren = 0.035 # kWh/m²/year
trg_RS_tes_scl     = ref_RS_tes_scl + acc_RS_tes_scl_ren*cur_RS_tes_scl_ren*(2050-2019) # [kWh/m²]
if post_process:
    print(f'Evolution of space cooling energy demand: +{round(-(1-(trg_RS_tes_scl)/(ref_RS_tes_scl))*100,1)}% [kWh/m²]')
    print(f' -> By 2019, the annual cooling energy demand is {round(ref_RS_tes_scl,2)} kWh/m²')
    print(f' -> By 2050, the annual cooling energy demand is {round(trg_RS_tes_scl,2)} kWh/m²')

Evolution of space cooling energy demand: +248.7% [kWh/m²]
 -> By 2019, the annual cooling energy demand is 0.44 kWh/m²
 -> By 2050, the annual cooling energy demand is 1.52 kWh/m²

# Inputs - Define Sufficiency Scenario Data (SUF)
shower_duration    = 5 # [min]
shower_flow_rate   = 7 # [l/min]
shower_temperature = 38 # [°C]
shower_energy      = shower_duration*shower_flow_rate*rho_h2o*cp_h2o*(shower_temperature-15) # [kWh/shower]
others_volume      = 10 # [l]
others_temperature = 60 # [°C]
others_energy      = others_volume*rho_h2o*cp_h2o*(others_temperature-15) # [kWh]
# Outputs - Sufficiency Scenario Data (SUF)
trg_RS_tes_shw     = (shower_energy+others_energy)*365
pro_RS_tes_shw     = (trg_RS_tes_shw-ref_RS_tes_shw)/ref_RS_tes_shw
if post_process:
    print(f'Evolution of sanitary hot water energy demand: {round(pro_RS_tes_shw*100,1)}% [kWh/person]')
    amount_2019 = ref_RS_tes_shw/(rho_h2o*cp_h2o*(40-15))/365 # equivalent l/day/person of 40°C water
    amount_2050 = trg_RS_tes_shw/(rho_h2o*cp_h2o*(40-15))/365 # equivalent l/day/person of 40°C water
    print(f' -> By 2019, the equivalent energy demand is {round(amount_2019,1)} liters of 40°C water per day and per person')
    print(f' -> By 2050, the equivalent energy demand is {round(amount_2050,1)} liters of 40°C water per day and per person')

Evolution of sanitary hot water energy demand: -21.2% [kWh/person]
 -> By 2019, the equivalent energy demand is 63.7 liters of 40°C water per day and per person
 -> By 2050, the equivalent energy demand is 50.2 liters of 40°C water per day and per person

# Inputs - Define Sufficiency Scenario Data (SUF)
pro_RS_tes_cok     = 0.15
ref_RS_tes_cok_gas = (3.846+21.957)/96.395 # ktoe_gas/ktoe_tot (from JRC-IDEES)
trg_RS_tes_cok_gas = 0.02
# Outputs - Sufficiency Scenario Data (SUF)
trg_RS_tes_cok = (1+pro_RS_tes_cok)*ref_RS_tes_cok # kWh/person
share_cook_gas = linear_growth(2019,  ref_RS_tes_cok_gas,
                               2050,  trg_RS_tes_cok_gas,years) # [%]
share_cook_ele = linear_growth(2019,1-ref_RS_tes_cok_gas,
                               2050,1-trg_RS_tes_cok_gas,years) # [%]
print(f'Evolution of cooking energy demand: {round(pro_RS_tes_cok*100,1)}% [kWh/household]')
print(f' -> By 2019, the equivalent energy demand is {round(ref_RS_tes_cok,2)} kWh per household')
print(f' -> By 2050, the equivalent energy demand is {round(trg_RS_tes_cok,2)} kWh per household')

Evolution of cooking energy demand: 15.0% [kWh/household]
 -> By 2019, the equivalent energy demand is 226.55 kWh per household
 -> By 2050, the equivalent energy demand is 260.54 kWh per household

ref_RS_tes_dhn = 10.823/(3664.049+663.435) # [%] 2019 reference share of district heating network in Belgium from JRC-IDEES
trg_RS_tes_dhn = 0.15                      # [%] 2050 target    share of district heating network in Belgium from nW-BE
share_heat_dhn = linear_growth(2019,  ref_RS_tes_dhn,
                               2050,  trg_RS_tes_dhn,years) # [%]
share_heat_ihs = linear_growth(2019,1-ref_RS_tes_dhn,
                               2050,1-trg_RS_tes_dhn,years) # [%]

# Inputs - Repartition of the electrical energy services (EES) in residential buildings for the reference year (2019)
ref_RS_ees_frg = 209.814*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_wsh =  64.045*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_dry =  73.446*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_dsh =  59.199*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_tvm = 257.507*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_ict =  96.860*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_lgt = 139.889*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
ref_RS_ees_oth =  97.004*ktoe_to_GWh*1e+6/df_SUF["households [household]"][2019] # [kWh/household]
if post_process:
    print(f'Energy service for refrigeration:    {round(ref_RS_ees_frg,3)} kWh/household')
    print(f'Energy service for washing machines: {round(ref_RS_ees_wsh,3)} kWh/household')
    print(f'Energy service for clothes dryer:    {round(ref_RS_ees_dry,3)} kWh/household')
    print(f'Energy service for dish washer:      {round(ref_RS_ees_dsh,3)} kWh/household')
    print(f'Energy service for TV and multimedia:{round(ref_RS_ees_tvm,3)} kWh/household')
    print(f'Energy service for ICT:              {round(ref_RS_ees_ict,3)} kWh/household')
    print(f'Energy service for lights:           {round(ref_RS_ees_lgt,3)} kWh/household')
    print(f'Energy service for others:           {round(ref_RS_ees_oth,3)} kWh/household')

Energy service for refrigeration:    493.117 kWh/household
Energy service for washing machines: 150.522 kWh/household
Energy service for clothes dryer:    172.617 kWh/household
Energy service for dish washer:      139.133 kWh/household
Energy service for TV and multimedia:605.207 kWh/household
Energy service for ICT:              227.646 kWh/household
Energy service for lights:           328.775 kWh/household
Energy service for others:           227.984 kWh/household

trg_RS_ees_frg = 150 # [kWh/household]
yea_RS_ees_frg = 2040
#2019+(ref_RS_ees_frg-trg_RS_ees_frg)/(16.967)

trg_RS_ees_wsh = 90 # [kWh/household]
yea_RS_ees_wsh = 2030
#2019+(ref_RS_ees_wsh-trg_RS_ees_wsh)/(6.277)

trg_RS_ees_dry = 80 # [kWh/household]
yea_RS_ees_dry = 2040
# 2019+(ref_RS_ees_dry-trg_RS_ees_dry)/(4.17)

trg_RS_ees_dsh =  150 # [kWh/household]
yea_RS_ees_dsh = 2050
#2019+(ref_RS_ees_dsh-trg_RS_ees_dsh)/(-0.351)

trg_RS_ees_tvm = (1-0.3)*ref_RS_ees_tvm # [kWh/household]
yea_RS_ees_tvm = 2050

trg_RS_ees_ict = ref_RS_ees_ict # [kWh/household]
yea_RS_ees_ict = 2019

trg_RS_ees_lgt = 130 # [kWh/household]
yea_RS_ees_lgt = 2035
# 2019+(ref_RS_ees_lgt-trg_RS_ees_lgt)/(12.853)

trg_RS_ees_oth = ref_RS_ees_oth # [kWh/household]
yea_RS_ees_oth = 2019

# Residential Sector - Thermal Energy Services
tes_RS_tot = {
    'space heating':      linear_growth(2019,ref_RS_tes_sht               *df_SUF["RS total surface [Mm²]"][2019]*1e-3,
                                        2050,trg_RS_tes_sht*suf_RS_tes_sht*df_SUF["RS total surface [Mm²]"][2050]*1e-3,years), # [TWh] kWh/m² * m²
    'space cooling':      linear_growth(2019,ref_RS_tes_scl               *df_SUF["RS total surface [Mm²]"][2019]*1e-3,
                                        2050,trg_RS_tes_scl               *df_SUF["RS total surface [Mm²]"][2050]*1e-3,years), # [TWh] kWh/m² * m²
    'sanitary hot water': linear_growth(2019,ref_RS_tes_shw               *df_SUF["population [person]"]   [2019]*1e-9,
                                        2050,trg_RS_tes_shw               *df_SUF["population [person]"]   [2050]*1e-9,years), # [TWh] kWh/person * person 
    'cooking':            linear_growth(2019,ref_RS_tes_cok               *df_SUF["households [household]"][2019]*1e-9,
                                        2050,trg_RS_tes_cok               *df_SUF["households [household]"][2050]*1e-9,years), # [TWh] kWh/household * household
    }
if post_process:
    total_TWh  = sum(val[0] for val in tes_RS_tot.values())
    total_ktoe = total_TWh*1e+3/ktoe_to_GWh
    print(f'-> Relative difference to JRC-IDEES for thermal services: {round(100*(4447.334-total_ktoe)/4447.334,3)}%')
    # === PLOT ===
    df_tes_RS_tot = pd.DataFrame(tes_RS_tot, index=years)
    dfts = df_tes_RS_tot
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfts))
    colors = ['tab:red','tab:blue','tab:orange','tab:green']
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, activity in enumerate(dfts.columns):
        ax.barh(dfts.index, dfts[activity],
                left=bottom, label=activity,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfts[activity]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Thermal Services in the Residential Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Service", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

-> Relative difference to JRC-IDEES for thermal services: -0.001%

# Residential Sector - Electrical Energy Services
ees_RS_tot = {
    'refrigeration':      linear_with_middle_point(2019,ref_RS_ees_frg*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_frg,trg_RS_ees_frg*df_SUF["households [household]"][yea_RS_ees_frg]*1e-9, 
                                                   2050,trg_RS_ees_frg*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'washing machines':   linear_with_middle_point(2019,ref_RS_ees_wsh*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_wsh,trg_RS_ees_wsh*df_SUF["households [household]"][yea_RS_ees_wsh]*1e-9, 
                                                   2050,trg_RS_ees_wsh*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'laundry dryers':     linear_with_middle_point(2019,ref_RS_ees_dry*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_dry,trg_RS_ees_dry*df_SUF["households [household]"][yea_RS_ees_dry]*1e-9, 
                                                   2050,trg_RS_ees_dry*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'dishwashers':        linear_with_middle_point(2019,ref_RS_ees_dsh*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_dsh,trg_RS_ees_dsh*df_SUF["households [household]"][yea_RS_ees_dsh]*1e-9, 
                                                   2050,trg_RS_ees_dsh*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'TV and multimedia':  linear_with_middle_point(2019,ref_RS_ees_tvm*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_tvm,trg_RS_ees_tvm*df_SUF["households [household]"][yea_RS_ees_tvm]*1e-9, 
                                                   2050,trg_RS_ees_tvm*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'ICT':                linear_with_middle_point(2019,ref_RS_ees_ict*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_ict,trg_RS_ees_ict*df_SUF["households [household]"][yea_RS_ees_ict]*1e-9, 
                                                   2050,trg_RS_ees_ict*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'light':              linear_with_middle_point(2019,ref_RS_ees_lgt*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_lgt,trg_RS_ees_lgt*df_SUF["households [household]"][yea_RS_ees_lgt]*1e-9, 
                                                   2050,trg_RS_ees_lgt*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    'others':             linear_with_middle_point(2019,ref_RS_ees_oth*df_SUF["households [household]"][2019]          *1e-9, 
                                         yea_RS_ees_oth,trg_RS_ees_oth*df_SUF["households [household]"][yea_RS_ees_oth]*1e-9, 
                                                   2050,trg_RS_ees_oth*df_SUF["households [household]"][2050]          *1e-9, years), # [TWh] kWh/household * household
    }
if post_process:
    total_TWh  = sum(val[0] for val in ees_RS_tot.values())
    total_ktoe = total_TWh*1e+3/ktoe_to_GWh
    print(f'-> Relative difference to JRC-IDEES for electrical services: {round(100*(997.764-total_ktoe)/997.764,3)}%')
    # === PLOT ===
    df_ees_RS_tot = pd.DataFrame(ees_RS_tot, index=years)
    dfes = df_ees_RS_tot
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfes))
    colors = plt.cm.tab10.colors # default matplotlib palette
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, activity in enumerate(dfes.columns):
        ax.barh(dfes.index, dfes[activity],
                left=bottom, label=activity,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfes[activity]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Electrical Services in the Residential Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Service", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

-> Relative difference to JRC-IDEES for electrical services: 0.009%

# End-Use Demand: thermal and electical
tes_RS_tot['heat_ihs']   = [(x+y)*z for x,y,z in zip(tes_RS_tot['space heating'], tes_RS_tot['sanitary hot water'], share_heat_ihs)]
tes_RS_tot['heat_dhn']   = [(x+y)*z for x,y,z in zip(tes_RS_tot['space heating'], tes_RS_tot['sanitary hot water'], share_heat_dhn)]
tes_RS_tot['cooking_ng'] = [ x*y    for x,y   in zip(tes_RS_tot['cooking'],                                         share_cook_gas)]
tes_RS_tot['cooking_el'] = [ x*y    for x,y   in zip(tes_RS_tot['cooking'],                                         share_cook_ele)]
df_tes_RS_tot = pd.DataFrame(tes_RS_tot, index=years) # [TWh]
df_ees_RS_tot = pd.DataFrame(ees_RS_tot, index=years) # [TWh]
# End-Use Demand: carrier distribution
df_eud_RS_tot_car = {
    'heat-ihs':    df_tes_RS_tot[["heat_ihs"]].T,
    'heat-dhn':    df_tes_RS_tot[["heat_dhn"]].T,
    'cold':        df_tes_RS_tot[["space cooling"]].T,
    'electricity': pd.concat([df_tes_RS_tot[["cooking_el"]], df_ees_RS_tot], axis=1).T,
    'fuel-gas':    df_tes_RS_tot[["cooking_ng"]].T,
    }
rows = []
for carrier, df in df_eud_RS_tot_car.items():
    temp = df.copy()
    temp['Carrier'] = carrier
    temp['Activity'] = temp.index
    temp = temp.reset_index(drop=True)
    rows.append(temp)
df_eud_RS_tot_car = pd.concat(rows, ignore_index=True)
# Carriers distribution - aggregated per carrier
df_eud_RS_tot_cln = df_eud_RS_tot_car.groupby('Carrier')[years].sum()
df_eud_RS_tot_cln = df_eud_RS_tot_cln.reset_index()
# Carriers distribution - normalisations
divider_series = df_SUF["households [household]"]
divider_series.index = divider_series.index.astype(float)
df_eud_RS_hsd_car = (df_eud_RS_tot_car[years].div(divider_series, axis=1))*1e+9
df_eud_RS_hsd_car[['Carrier', 'Activity']] = df_eud_RS_tot_car[['Carrier', 'Activity']]

if post_process:
    # === Table with values ===
    df_abs = df_eud_RS_tot_car.set_index(['Carrier', 'Activity'])[years]
    df_rel = df_eud_RS_hsd_car.set_index(['Carrier', 'Activity'])[years]
    df_abs['Unit'] = 'TWh'
    df_rel['Unit'] = 'kWh/household'
    df_combined = pd.concat([df_abs, df_rel]).set_index('Unit', append=True)
    df_combined = df_combined.sort_index(level=['Carrier', 'Activity'], sort_remaining=False)
    df_final = df_combined.reset_index()
    df_final['Carrier'] = df_final['Carrier'].mask(df_final['Carrier'].duplicated(), '')
    df_final['Activity'] = df_final['Activity'].mask(df_final['Activity'].duplicated(), '')
    styled = (
        df_final.style
        .hide(axis='index')
        .apply(highlight_mode_separator, axis=1)
        .set_properties(subset=['Unit'], **{'font-size': '85%', 'color': 'gray'})
        .set_properties(subset=['Activity'], **{'font-style':'italic'})
        .format({year: "{:.3f}" for year in years})
        .set_table_attributes('style="width:100%;table-layout:fixed;"')
        .apply(lambda row: [bold_mode(cell, row['Carrier'], col) for col, cell in zip(df_final.columns, row)], axis=1)
    )
    display(styled)   
    # === Breakdown bar chart ===
    dfrs = df_eud_RS_tot_cln.set_index('Carrier').reindex(['heat-ihs', 'heat-dhn', 'cold', 'electricity', 'fuel-gas']).transpose()
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfrs))
    colors = ['tab:red','darkred','tab:blue','tab:green','tab:orange']
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, carrier in enumerate(dfrs.columns):
        ax.barh(dfrs.index, dfrs[carrier],
                left=bottom, label=carrier,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfrs[carrier]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Residential Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Carrier", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

# Inputs - Repartition of the thermal energy services (TES) in tertiary buildings for the reference year (2019)
ref_TS_tes_sht = 1862.949*ktoe_to_GWh*1e+6/ref_TS_sur_tot # [kWh/m²]
ref_TS_tes_scl =  336.272*ktoe_to_GWh*1e+6/ref_TS_sur_tot # [kWh/m²]
ref_TS_tes_shw =  299.836*ktoe_to_GWh*1e+6/population_dict[2019] # [kWh/person]
ref_TS_tes_cat =  284.204*ktoe_to_GWh*1e+6/population_dict[2019] # [kWh/person]
if post_process:
    print(f'Energy service for space heating: {round(ref_TS_tes_sht,3)} kWh/m²')
    print(f'Energy service for space cooling: {round(ref_TS_tes_scl,3)} kWh/m²')
    print(f'Energy service for hot water:    {round(ref_TS_tes_shw,3)} kWh/person')
    print(f'Energy service for catering:     {round(ref_TS_tes_cat,3)} kWh/person')

Energy service for space heating: 95.415 kWh/m²
Energy service for space cooling: 17.223 kWh/m²
Energy service for hot water:    305.045 kWh/person
Energy service for catering:     289.141 kWh/person

acc_TS_tes_sht_ren = 5
cur_TS_tes_sht_ren = -0.154 # kWh/m²/year
trg_TS_tes_sht     = ref_TS_tes_sht + acc_TS_tes_sht_ren*cur_TS_tes_sht_ren*(2050-2019) # [kWh/m²]

d_cons_temp = 0.07
d_temp = 1
suf_TS_tes_sht = 1-d_temp*d_cons_temp
if post_process:
    print(f'Evolution of space heating energy demand: {round(-(1-(trg_TS_tes_sht*suf_TS_tes_sht)/(ref_TS_tes_sht))*100,1)}% [kWh/m²]')
    print(f' -> By 2019, the annual heating energy demand is {round(ref_TS_tes_sht,               2)} kWh/m²')
    print(f' -> By 2050, the annual heating energy demand is {round(trg_TS_tes_sht*suf_TS_tes_sht,2)} kWh/m²')

Evolution of space heating energy demand: -30.3% [kWh/m²]
 -> By 2019, the annual heating energy demand is 95.41 kWh/m²
 -> By 2050, the annual heating energy demand is 66.54 kWh/m²

acc_TS_tes_scl_ren = 1/3
cur_TS_tes_scl_ren = 0.743 # kWh/m²/year
trg_TS_tes_scl     = ref_TS_tes_scl + acc_TS_tes_scl_ren*cur_TS_tes_scl_ren*(2050-2019) # [kWh/m²]
if post_process:
    print(f'Evolution of space cooling energy demand: +{round(-(1-(trg_TS_tes_scl)/(ref_TS_tes_scl))*100,1)}% [kWh/m²]')
    print(f' -> By 2019, the annual cooling energy demand is {round(ref_TS_tes_scl,2)} kWh/m²')
    print(f' -> By 2050, the annual cooling energy demand is {round(trg_TS_tes_scl,2)} kWh/m²')

Evolution of space cooling energy demand: +44.6% [kWh/m²]
 -> By 2019, the annual cooling energy demand is 17.22 kWh/m²
 -> By 2050, the annual cooling energy demand is 24.9 kWh/m²

# Inputs - Define Sufficiency Scenario Data (SUF)
pro_TS_tes_shw     = 0.00
# Outputs - Sufficiency Scenario Data (SUF)
trg_TS_tes_shw     = (1+pro_TS_tes_shw)*ref_TS_tes_shw # kWh/person
print(f'Evolution of sanitary hot water consumption: {round(pro_TS_tes_shw*100,1)}% [kWh/person]')
print(f' -> By 2019, the equivalent consumption is {round(ref_TS_tes_shw,2)} kWh per person')
print(f' -> By 2050, the equivalent consumption is {round(trg_TS_tes_shw,2)} kWh per person')

Evolution of sanitary hot water consumption: 0.0% [kWh/person]
 -> By 2019, the equivalent consumption is 305.04 kWh per person
 -> By 2050, the equivalent consumption is 305.04 kWh per person

# Inputs - Define Sufficiency Scenario Data (SUF)
pro_TS_tes_cat     = 0.20
ref_TS_tes_cat_gas = (8.421+106.309)/284.204 # ktoe_gas/ktoe_tot (from JRC-IDEES)
trg_TS_tes_cat_gas = 0.050
ref_TS_tes_cat_bio =          1.603 /284.204 # ktoe_bio/ktoe_tot (from JRC-IDEES)
trg_TS_tes_cat_bio = ref_TS_tes_cat_bio
# Outputs - Sufficiency Scenario Data (SUF)
trg_TS_tes_cat     = (1+pro_TS_tes_cat)*ref_TS_tes_cat # kWh/person
share_ctrg_gas = linear_growth(2019,  ref_TS_tes_cat_gas,
                               2050,  trg_TS_tes_cat_gas,years) # [%]
share_ctrg_bio = linear_growth(2019,  ref_TS_tes_cat_bio,
                               2050,  trg_TS_tes_cat_bio,years) # [%]
share_ctrg_ele = linear_growth(2019,1-ref_TS_tes_cat_gas-ref_TS_tes_cat_bio,
                               2050,1-trg_TS_tes_cat_gas-trg_TS_tes_cat_bio,years) # [%]
print(f'Evolution of catering consumption: {round(pro_TS_tes_cat*100,1)}% [kWh/person]')
print(f' -> By 2019, the equivalent consumption is {round(ref_TS_tes_cat,2)} kWh per person')
print(f' -> By 2050, the equivalent consumption is {round(trg_TS_tes_cat,2)} kWh per person')

Evolution of catering consumption: 20.0% [kWh/person]
 -> By 2019, the equivalent consumption is 289.14 kWh per person
 -> By 2050, the equivalent consumption is 346.97 kWh per person

ref_TS_tes_dhn = (46.381+5.984)/(1862.949+299.836) # [%] 2019 reference share of district heating network in Belgium from JRC-IDEES
trg_TS_tes_dhn = 0.15                              # [%] 2050 target    share of district heating network in Belgium from nW-BE
share_heat_dhn = linear_growth(2019,  ref_TS_tes_dhn,
                               2050,  trg_TS_tes_dhn,years) # [%]
share_heat_ihs = linear_growth(2019,1-ref_TS_tes_dhn,
                               2050,1-trg_TS_tes_dhn,years) # [%]

# Inputs - Repartition of the electrical energy services (EES) in tertiary buildings for the reference year (2019)
ref_TS_ees_vnt =  71.632*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_TS_ees_slt =  77.819*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_TS_ees_blt = 297.768*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_TS_ees_frg = 179.674*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_TS_ees_msc = 176.613*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
ref_TS_ees_ict = 248.983*ktoe_to_GWh*1e+6/df_SUF["population [person]"][2019] # [kWh/person]
if post_process:
    print(f'Energy service for ventilation:        {round(ref_TS_ees_vnt,3)} kWh/person')
    print(f'Energy service for street lighting:    {round(ref_TS_ees_slt,3)} kWh/person')
    print(f'Energy service for building lighting: {round(ref_TS_ees_blt,3)} kWh/person')
    print(f'Energy service for refrigeration:     {round(ref_TS_ees_frg,3)} kWh/person')
    print(f'Energy service for miscellaneous:     {round(ref_TS_ees_msc,3)} kWh/person')
    print(f'Energy service for ICT & multimedia:  {round(ref_TS_ees_ict,3)} kWh/person')

Energy service for ventilation:        72.876 kWh/person
Energy service for street lighting:    79.171 kWh/person
Energy service for building lighting: 302.941 kWh/person
Energy service for refrigeration:     182.795 kWh/person
Energy service for miscellaneous:     179.681 kWh/person
Energy service for ICT & multimedia:  253.308 kWh/person

trg_TS_ees_vnt = ref_TS_ees_vnt # [kWh/person]

trg_TS_ees_slt = ref_TS_ees_slt-0.476*(2050-2019) # [kWh/person]

trg_TS_ees_blt = 150 # [kWh/person]

pro_TS_ees_frg = -0.15
trg_TS_ees_frg = (1+pro_TS_ees_frg)*ref_TS_ees_frg # [kWh/person]

trg_TS_ees_msc = ref_TS_ees_msc # [kWh/person]

pro_TS_ees_ict = 0.10
trg_TS_ees_ict = (1+pro_TS_ees_ict)*ref_TS_ees_ict # [kWh/person]

# Tertiary Sector - Thermal Energy Services
tes_TS_tot = {
    'space heating':      linear_growth(2019,ref_TS_tes_sht               *df_SUF["TS total surface [Mm²]"][2019]*1e-3,
                                        2050,trg_TS_tes_sht*suf_TS_tes_sht*df_SUF["TS total surface [Mm²]"][2050]*1e-3,years), # [TWh] kWh/m² * m²
    'space cooling':      linear_growth(2019,ref_TS_tes_scl               *df_SUF["TS total surface [Mm²]"][2019]*1e-3,
                                        2050,trg_TS_tes_scl               *df_SUF["TS total surface [Mm²]"][2050]*1e-3,years), # [TWh] kWh/m² * m²
    'sanitary hot water': linear_growth(2019,ref_TS_tes_shw               *df_SUF["population [person]"]   [2019]*1e-9,
                                        2050,trg_TS_tes_shw               *df_SUF["population [person]"]   [2050]*1e-9,years), # [TWh] kWh/person * person 
    'catering':           linear_growth(2019,ref_TS_tes_cat               *df_SUF["population [person]"]   [2019]*1e-9,
                                        2050,trg_TS_tes_cat               *df_SUF["population [person]"]   [2050]*1e-9,years), # [TWh] kWh/person * person
    }
if post_process:
    total_TWh  = sum(val[0] for val in tes_TS_tot.values())
    total_ktoe = total_TWh*1e+3/ktoe_to_GWh
    print(f'-> Relative difference to JRC-IDEES for thermal services: {round(100*(2783.260-total_ktoe)/2783.260,3)}%')
    # === PLOT ===
    df_tes_TS_tot = pd.DataFrame(tes_TS_tot, index=years)
    dfts = df_tes_TS_tot
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfts))
    colors = ['tab:red','tab:blue','tab:orange','tab:green']
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, activity in enumerate(dfts.columns):
        ax.barh(dfts.index, dfts[activity],
                left=bottom, label=activity,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfts[activity]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Thermal Services in the Tertiary Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Service", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

-> Relative difference to JRC-IDEES for thermal services: 0.001%

# Tertiary Sector - Electrical Energy Services
ees_TS_tot = {
    'ventilation':        linear_growth(2019,           ref_TS_ees_vnt*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_vnt*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
    'street lighting':    linear_growth(2019,           ref_TS_ees_slt*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_slt*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
    'building lighting':  linear_growth(2019,           ref_TS_ees_blt*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_blt*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
    'refrigeration':      linear_growth(2019,           ref_TS_ees_frg*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_frg*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
    'miscellaneous':      linear_growth(2019,           ref_TS_ees_msc*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_msc*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
    'ICT':                linear_growth(2019,           ref_TS_ees_ict*df_SUF["population [person]"][2019]          *1e-9, 
                                        2050,           trg_TS_ees_ict*df_SUF["population [person]"][2050]          *1e-9, years), # [TWh] kWh/person * person
}
if post_process:
    total_TWh  = sum(val[0] for val in ees_TS_tot.values())
    total_ktoe = total_TWh*1e+3/ktoe_to_GWh
    print(f'-> Relative difference to JRC-IDEES for electrical services: {round(100*(1052.489-total_ktoe)/1052.489,3)}%')
    # === PLOT ===
    df_ees_TS_tot = pd.DataFrame(ees_TS_tot, index=years)
    dfes = df_ees_TS_tot
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfes))
    colors = plt.cm.tab10.colors # default matplotlib palette
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, activity in enumerate(dfes.columns):
        ax.barh(dfes.index, dfes[activity],
                left=bottom, label=activity,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfes[activity]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Electrical Services in the Tertiary Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Service", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

-> Relative difference to JRC-IDEES for electrical services: -0.005%

# End-Use Demand: thermal and electical
tes_TS_tot['heat_ihs']   = [(x+y)*z for x,y,z in zip(tes_TS_tot['space heating'], tes_TS_tot['sanitary hot water'], share_heat_ihs)]
tes_TS_tot['heat_dhn']   = [(x+y)*z for x,y,z in zip(tes_TS_tot['space heating'], tes_TS_tot['sanitary hot water'], share_heat_dhn)]
tes_TS_tot['cooking_ng'] = [ x*y    for x,y   in zip(tes_TS_tot['catering'],                                        share_ctrg_gas)]
tes_TS_tot['cooking_bm'] = [ x*y    for x,y   in zip(tes_TS_tot['catering'],                                        share_ctrg_bio)]
tes_TS_tot['cooking_el'] = [ x*y    for x,y   in zip(tes_TS_tot['catering'],                                        share_ctrg_ele)]
df_tes_TS_tot = pd.DataFrame(tes_TS_tot, index=years) # [TWh]
df_ees_TS_tot = pd.DataFrame(ees_TS_tot, index=years) # [TWh]
# End-Use Demand: carrier distribution
df_eud_TS_tot_car = {
    'heat-ihs':    df_tes_TS_tot[["heat_ihs"]].T,
    'heat-dhn':    df_tes_TS_tot[["heat_dhn"]].T,
    'cold':        df_tes_TS_tot[["space cooling"]].T,
    'electricity': pd.concat([df_tes_TS_tot[["cooking_el"]], df_ees_TS_tot], axis=1).T,
    'fuel-gas':    df_tes_TS_tot[["cooking_ng"]].T,
	'fuel-bio':    df_tes_TS_tot[["cooking_bm"]].T,
    }
rows = []
for carrier, df in df_eud_TS_tot_car.items():
    temp = df.copy()
    temp['Carrier'] = carrier
    temp['Activity'] = temp.index
    temp = temp.reset_index(drop=True)
    rows.append(temp)
df_eud_TS_tot_car = pd.concat(rows, ignore_index=True)
# Carriers distribution - aggregated per carrier
df_eud_TS_tot_cln = df_eud_TS_tot_car.groupby('Carrier')[years].sum()
df_eud_TS_tot_cln = df_eud_TS_tot_cln.reset_index()
# Carriers distribution - normalisations
divider_series = df_SUF["population [person]"]
divider_series.index = divider_series.index.astype(float)
df_eud_TS_hsd_car = (df_eud_TS_tot_car[years].div(divider_series, axis=1))*1e+9
df_eud_TS_hsd_car[['Carrier', 'Activity']] = df_eud_TS_tot_car[['Carrier', 'Activity']]
if post_process:
    # === Table with values ===
    df_abs = df_eud_TS_tot_car.set_index(['Carrier', 'Activity'])[years]
    df_rel = df_eud_TS_hsd_car.set_index(['Carrier', 'Activity'])[years]
    df_abs['Unit'] = 'TWh'
    df_rel['Unit'] = 'kWh/person'
    df_combined = pd.concat([df_abs, df_rel]).set_index('Unit', append=True)
    df_combined = df_combined.sort_index(level=['Carrier', 'Activity'], sort_remaining=False)
    df_final = df_combined.reset_index()
    df_final['Carrier'] = df_final['Carrier'].mask(df_final['Carrier'].duplicated(), '')
    df_final['Activity'] = df_final['Activity'].mask(df_final['Activity'].duplicated(), '')
    styled = (
        df_final.style
        .hide(axis='index')
        .apply(highlight_mode_separator, axis=1)
        .set_properties(subset=['Unit'], **{'font-size': '85%', 'color': 'gray'})
        .set_properties(subset=['Activity'], **{'font-style':'italic'})
        .format({year: "{:.3f}" for year in years})
        .set_table_attributes('style="width:100%;table-layout:fixed;"')
        .apply(lambda row: [bold_mode(cell, row['Carrier'], col) for col, cell in zip(df_final.columns, row)], axis=1)
    )
    display(styled)   
    # === Breakdown bar chart ===
    dfrs = df_eud_TS_tot_cln.set_index('Carrier').reindex(['heat-ihs', 'heat-dhn', 'cold', 'electricity', 'fuel-gas', 'fuel-bio']).transpose()
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfrs))
    colors = ['tab:red','darkred','tab:blue','tab:orange','tab:green','tab:brown']
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, carrier in enumerate(dfrs.columns):
        ax.barh(dfrs.index, dfrs[carrier],
                left=bottom, label=carrier,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfrs[carrier]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Tertiary Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Carrier", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

# Aggregate global demand data
df_eud_BS_tot_cln = pd.concat([df_eud_RS_tot_cln, df_eud_TS_tot_cln])
df_eud_BS_tot_fin = df_eud_BS_tot_cln.groupby('Carrier').sum().reset_index()
df_eud_BS_tot_fin.loc[ len(df_eud_BS_tot_fin)] = df_eud_BS_tot_fin.sum(numeric_only=True)
df_eud_BS_tot_fin.iloc[-1, df_eud_BS_tot_fin.columns.get_loc('Carrier')] = 'TOTAL'
df_eud_BS_tot_fin = df_eud_BS_tot_fin.set_index('Carrier')
custom_order = [
    'heat-dhn', 
    'heat-ihs', 
    'cold', 
    'electricity', 
    'fuel-gas', 
    'fuel-bio', 
    'TOTAL'
]
df_eud_BS_tot_fin = df_eud_BS_tot_fin.reindex([c for c in custom_order if c in df_eud_BS_tot_fin.index])
df_eud_BS_tot_fin

if post_process:
    dfbs = df_eud_BS_tot_fin.loc[['heat-ihs', 'heat-dhn', 'cold', 'electricity', 'fuel-gas', 'fuel-bio']].transpose()
    fig1, ax = plt.subplots(figsize=(14, 5))
    bottom = np.zeros(len(dfbs))
    colors = ['tab:red','darkred','tab:blue','tab:orange','tab:green','tab:brown']
    # Thicker bars → control via height argument
    bar_height = 2.0  # default is ~0.8; increase to 0.9–1.0 for thicker bars
    for i, carrier in enumerate(dfbs.columns):
        ax.barh(dfbs.index, dfbs[carrier],
                left=bottom, label=carrier,
                color=colors[i % len(colors)],
                height=bar_height)
        bottom += dfbs[carrier]
    ax.set_xlabel("End-Use Demand [TWh]", fontsize=10, ha='right', va='top')
    ax.xaxis.set_label_coords(1, -0.1) 
    ax.set_ylabel("Years", fontsize=10, rotation=0, ha='left', va='bottom')
    ax.yaxis.set_label_coords(-0.05, 1)
    ax.set_yticks(years)
    ax.yaxis.set_inverted(True) # inverted axis with autoscaling
    ax.set_title("Evolution of End-Use Demand for Building Sector (negaWatt-BE Scenario)", fontsize=11, pad=15)
    ax.legend(bbox_to_anchor=(1, 1), loc='upper left', title="Carrier", frameon=False)
    ax.grid(axis='x', linestyle='--', alpha=0.6)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)
    fig1.tight_layout()
    plt.show()

# ===== Website export: build website/data/buildings.js =====
# Numbers are read from the assumption variables defined above, so editing an
# assumption and re-running the notebook refreshes the public website.

_NB = "../notebooks/nW_BE_demand_model_buildings.html"
_H = []
def _add(*a, **k):
    _H.append(make_hypothesis(*a, **k))

# --- Residential (RS) ---
_add("rs-surface", "Residential floor area per person", "Residential / sufficiency",
     round(ref_RS_sur_spe, 1), round(ref_RS_sur_spe * (1 + pro_RS_sur_spe), 1),
     unit="m\u00b2/person", notebook=_NB, reference="JRC-IDEES; shared living, downsizing")
_add("rs-heating", "Home space heating", "Residential / thermal",
     round(ref_RS_tes_sht, 1), round(trg_RS_tes_sht * suf_RS_tes_sht, 1),
     unit="kWh/m\u00b2", notebook=_NB, reference="Renovation (x2 rate) + SlowHeat (-2\u00b0C)")
_add("rs-cooling", "Home space cooling", "Residential / thermal",
     round(ref_RS_tes_scl, 2), round(trg_RS_tes_scl, 2),
     unit="kWh/m\u00b2", notebook=_NB, reference="Controlled AC deployment")
_add("rs-hotwater", "Domestic hot water", "Residential / thermal",
     round(ref_RS_tes_shw), round(trg_RS_tes_shw), unit="kWh/person",
     notebook=_NB, reference="5-min showers; sufficient use")
_add("rs-cooking", "Home cooking demand", "Residential / thermal",
     round(ref_RS_tes_cok), round(trg_RS_tes_cok), unit="kWh/household",
     notebook=_NB, reference="More home cooking, less processed food")
_add("rs-cooking-gas", "Gas share in home cooking", "Residential / carrier",
     round(ref_RS_tes_cok_gas * 100, 1), round(trg_RS_tes_cok_gas * 100, 1),
     unit="% gas", notebook=_NB, reference="Electrification of cooking")
_add("rs-dhn", "District heating (residential)", "Residential / carrier",
     round(ref_RS_tes_dhn * 100, 1), round(trg_RS_tes_dhn * 100, 1),
     unit="% of heat", notebook=_NB, reference="Lund et al.; EnergyVille PATHS2050")
_add("rs-fridge", "Refrigeration", "Residential / appliances",
     round(ref_RS_ees_frg), round(trg_RS_ees_frg), unit="kWh/household",
     notebook=_NB, reference="EU Ecodesign")
_add("rs-washing", "Washing machines", "Residential / appliances",
     round(ref_RS_ees_wsh), round(trg_RS_ees_wsh), unit="kWh/household",
     notebook=_NB, reference="EU Ecodesign; eco programmes")
_add("rs-dryer", "Laundry dryers", "Residential / appliances",
     round(ref_RS_ees_dry), round(trg_RS_ees_dry), unit="kWh/household",
     notebook=_NB, reference="Heat-pump dryers; passive drying")
_add("rs-dishwasher", "Dishwashers", "Residential / appliances",
     round(ref_RS_ees_dsh), round(trg_RS_ees_dsh), unit="kWh/household",
     notebook=_NB, reference="Continued deployment")
_add("rs-tv", "TV & multimedia", "Residential / appliances",
     round(ref_RS_ees_tvm), round(trg_RS_ees_tvm), unit="kWh/household",
     notebook=_NB, reference="EU Ecodesign")
_add("rs-ict", "ICT equipment", "Residential / appliances",
     round(ref_RS_ees_ict), round(trg_RS_ees_ict), unit="kWh/household",
     notebook=_NB, reference="Stabilised demand")
_add("rs-lighting", "Lighting", "Residential / appliances",
     round(ref_RS_ees_lgt), round(trg_RS_ees_lgt), unit="kWh/household",
     notebook=_NB, reference="LED efficiency")
_add("rs-other", "Other appliances", "Residential / appliances",
     round(ref_RS_ees_oth), round(trg_RS_ees_oth), unit="kWh/household",
     notebook=_NB, reference="Held constant")

# --- Tertiary (TS) ---
_add("ts-surface", "Tertiary floor area per person", "Tertiary / sufficiency",
     round(ref_TS_sur_spe, 1), round(ref_TS_sur_spe * (1 + pro_TS_sur_spe), 1),
     unit="m\u00b2/person", notebook=_NB, reference="Telework, space rationalisation")
_add("ts-heating", "Tertiary space heating", "Tertiary / thermal",
     round(ref_TS_tes_sht, 1), round(trg_TS_tes_sht * suf_TS_tes_sht, 1),
     unit="kWh/m\u00b2", notebook=_NB, reference="Renovation (x5 rate) + SlowHeat (-1\u00b0C)")
_add("ts-cooling", "Tertiary space cooling", "Tertiary / thermal",
     round(ref_TS_tes_scl, 1), round(trg_TS_tes_scl, 1), unit="kWh/m\u00b2",
     notebook=_NB, reference="Controlled AC deployment")
_add("ts-hotwater", "Tertiary hot water", "Tertiary / thermal",
     round(ref_TS_tes_shw), round(trg_TS_tes_shw), unit="kWh/person",
     notebook=_NB, reference="Contain rising trend")
_add("ts-catering", "Catering demand", "Tertiary / thermal",
     round(ref_TS_tes_cat), round(trg_TS_tes_cat), unit="kWh/person",
     notebook=_NB, reference="Growing out-of-home dining")
_add("ts-catering-gas", "Gas share in catering", "Tertiary / carrier",
     round(ref_TS_tes_cat_gas * 100, 1), round(trg_TS_tes_cat_gas * 100, 1),
     unit="% gas", notebook=_NB, reference="Electrification (some biomass kept)")
_add("ts-dhn", "District heating (tertiary)", "Tertiary / carrier",
     round(ref_TS_tes_dhn * 100, 1), round(trg_TS_tes_dhn * 100, 1),
     unit="% of heat", notebook=_NB, reference="Same 15% target as residential")
_add("ts-ventilation", "Ventilation", "Tertiary / electrical",
     round(ref_TS_ees_vnt), round(trg_TS_ees_vnt), unit="kWh/person",
     notebook=_NB, reference="Held constant")
_add("ts-streetlight", "Street lighting", "Tertiary / electrical",
     round(ref_TS_ees_slt), round(trg_TS_ees_slt), unit="kWh/person",
     notebook=_NB, reference="LED; less over-lighting")
_add("ts-buildinglight", "Building lighting", "Tertiary / electrical",
     round(ref_TS_ees_blt), round(trg_TS_ees_blt), unit="kWh/person",
     notebook=_NB, reference="Efficiency gains")
_add("ts-fridge", "Commercial refrigeration", "Tertiary / electrical",
     round(ref_TS_ees_frg), round(trg_TS_ees_frg), unit="kWh/person",
     notebook=_NB, reference="Door-fitted fridges")
_add("ts-misc", "Miscellaneous building tech", "Tertiary / electrical",
     round(ref_TS_ees_msc), round(trg_TS_ees_msc), unit="kWh/person",
     notebook=_NB, reference="Held constant")
_add("ts-ict", "Tertiary ICT & multimedia", "Tertiary / electrical",
     round(ref_TS_ees_ict), round(trg_TS_ees_ict), unit="kWh/person",
     notebook=_NB, reference="Growing trend")

write_hypotheses_js("buildings", _H, title="Buildings (residential & tertiary)")

# Pin buildings energy_totals overrides so CI can compare notebooks vs nW_BE.py.
_ov_path = make_energy_totals_overrides(
    energy_totals_overrides_from_buildings(
        df_tes_RS_tot, df_ees_RS_tot, df_tes_TS_tot, df_ees_TS_tot
    ),
    replace_sources=["buildings"],
    comment="generated from nW_BE demand-model notebooks; CI compares nW_BE.py to this file",
)
print(f"[energy_totals] wrote buildings rows to {_ov_path}")

import pandas as _pd
_pd.DataFrame([{ "id": h["id"], "hypothesis": h["name"], "category": h["category"],
                 "2019": h["refValue"], "2050": h["targetValue"], "unit": h["unit"] }
               for h in _H])

# ===== Workshop export: build website/data/levers_buildings.js =====
# See docs/workshop_module.md. The levers are defined in workshop_levers/,
# one module per topic (residential-heat, tertiary-heat).

from workshop_levers import export_topics, topic_modules

_lev_path = export_topics("buildings", globals(), title="Buildings")

import pandas as _pd
_pd.DataFrame([{"topic": _m.TOPIC, "module": _m.__name__.split(".")[-1],
                "levers": len(_m.build(globals())["levers"])}
               for _m in topic_modules("buildings")])

Sector	Parameter	2019	2025	2030	2035	2040	2045	2050
Demographic	population [person]	11431406.000	11816102.000	12023862.000	12186730.000	12347171.000	12490658.000	12600911.000
	households [household]	4948398.000	5199667.000	5355123.000	5489927.000	5609698.000	5702818.000	5770867.000
	households [person]	2.310	2.272	2.245	2.220	2.201	2.190	2.184
Residential	RS total surface [Mm²]	625.241	633.768	634.307	632.150	629.570	625.869	620.280
	RS household surface [m²/household]	126.352	121.886	118.449	115.147	112.229	109.747	107.485
	RS specific surface [m²/person]	54.695	53.636	52.754	51.872	50.989	50.107	49.225
Tertiary	TS total surface [Mm²]	227.073	230.178	230.365	229.586	228.645	227.305	225.279
	TS specific surface [m²/person]	19.864	19.480	19.159	18.839	18.518	18.198	17.878

Carrier	Activity	Unit	2019	2025	2030	2035	2040	2045	2050
cold	space cooling	TWh	0.273	0.403	0.511	0.619	0.727	0.835	0.944
		kWh/household	55.169	77.505	95.423	112.752	129.597	146.419	163.580
electricity	ICT	TWh	1.126	1.163	1.193	1.223	1.253	1.284	1.314
		kWh/household	227.548	223.668	222.777	222.772	223.363	225.152	227.695
	TV and multimedia	TWh	2.995	2.888	2.800	2.711	2.622	2.534	2.445
		kWh/household	605.246	555.420	522.864	493.813	467.405	444.342	423.680
	cooking_el	TWh	0.821	0.932	1.031	1.133	1.242	1.355	1.474
		kWh/household	165.826	179.261	192.477	206.465	221.402	237.686	255.407
	dishwashers	TWh	0.688	0.723	0.751	0.780	0.808	0.837	0.866
		kWh/household	139.035	139.047	140.240	142.078	144.036	146.770	150.064
	laundry dryers	TWh	0.854	0.738	0.642	0.545	0.449	0.455	0.462
		kWh/household	172.581	141.932	119.885	99.273	80.040	79.785	80.057
	light	TWh	1.627	1.284	0.999	0.714	0.726	0.738	0.750
		kWh/household	328.793	246.939	186.550	130.056	129.419	129.410	129.963
	others	TWh	1.128	1.164	1.195	1.225	1.255	1.285	1.316
		kWh/household	227.953	223.860	223.151	223.136	223.720	225.327	228.042
	refrigeration	TWh	2.440	1.983	1.603	1.222	0.841	0.854	0.866
		kWh/household	493.089	381.371	299.340	222.589	149.919	149.751	150.064
	washing machines	TWh	0.745	0.601	0.482	0.491	0.501	0.510	0.519
		kWh/household	150.554	115.584	90.007	89.437	89.310	89.429	89.934
fuel-gas	cooking_ng	TWh	0.300	0.263	0.226	0.185	0.138	0.087	0.030
		kWh/household	60.712	50.561	42.251	33.611	24.600	15.171	5.212
heat-dhn	heat_dhn	TWh	0.151	1.426	2.331	3.062	3.585	3.972	4.187
		kWh/household	30.512	274.189	435.204	557.742	638.998	696.568	725.480
heat-ihs	heat_ihs	TWh	50.178	44.564	40.043	35.697	31.558	27.555	23.724
		kWh/household	10140.254	8570.608	7477.593	6502.279	5625.689	4831.751	4111.055

Carrier	Activity	Unit	2019	2025	2030	2035	2040	2045	2050
cold	space cooling	TWh	3.911	4.240	4.514	4.788	5.062	5.336	5.610
		kWh/person	342.128	358.832	375.420	392.886	409.972	427.199	445.206
electricity	ICT	TWh	2.896	3.015	3.114	3.213	3.313	3.412	3.511
		kWh/person	253.337	255.160	258.985	263.647	268.321	273.164	278.631
	building lighting	TWh	3.463	3.159	2.905	2.651	2.398	2.144	1.890
		kWh/person	302.937	267.347	241.603	217.532	194.215	171.648	149.989
	cooking_el	TWh	1.953	2.262	2.535	2.818	3.115	3.424	3.744
		kWh/person	170.867	191.463	210.801	231.264	252.284	274.110	297.114
	miscellaneous	TWh	2.054	2.095	2.129	2.162	2.196	2.230	2.264
		kWh/person	179.680	177.300	177.065	177.406	177.855	178.533	179.670
	refrigeration	TWh	2.090	2.064	2.043	2.022	2.000	1.979	1.958
		kWh/person	182.830	174.677	169.912	165.918	161.980	158.438	155.386
	street lighting	TWh	0.905	0.887	0.872	0.857	0.842	0.827	0.812
		kWh/person	79.168	75.067	72.522	70.322	68.194	66.209	64.440
	ventilation	TWh	0.833	0.850	0.863	0.877	0.891	0.905	0.918
		kWh/person	72.869	71.936	71.774	71.964	72.162	72.454	72.852
fuel-bio	cooking_bm	TWh	0.020	0.021	0.021	0.022	0.023	0.023	0.024
		kWh/person	1.735	1.743	1.766	1.795	1.824	1.854	1.888
fuel-gas	cooking_ng	TWh	1.335	1.150	0.984	0.806	0.615	0.413	0.198
		kWh/person	116.803	97.329	81.847	66.118	49.849	33.066	15.737
heat-dhn	heat_dhn	TWh	0.604	1.173	1.581	1.948	2.275	2.581	2.825
		kWh/person	52.808	99.235	131.477	159.871	184.257	206.615	224.186
heat-ihs	heat_ihs	TWh	24.549	22.757	21.330	19.943	18.597	17.271	16.008
		kWh/person	2147.534	1925.968	1773.984	1636.428	1506.171	1382.733	1270.388

	2019	2025	2030	2035	2040	2045	2050
Carrier
heat-dhn	0.754659	2.598260	3.911429	5.010260	5.859634	6.553162	7.011600
heat-ihs	74.727341	67.321740	61.373571	55.639740	50.155366	44.825838	39.732400
cold	4.184000	4.643000	5.025000	5.407000	5.789000	6.171000	6.554000
electricity	26.617827	25.808447	25.156380	24.644838	24.451990	24.773300	25.108824
fuel-gas	1.635648	1.412955	1.210380	0.990286	0.753492	0.499540	0.228380
fuel-bio	0.019830	0.020598	0.021240	0.021876	0.022518	0.023160	0.023796
TOTAL	107.939305	101.805000	96.698000	91.714000	87.032000	82.846000	78.659000

Input Assumptions for Modelling the Building Sector in the negaWatt-BE Scenario

ENERGY DEMAND FOR THE BUILDING SECTOR IN BELGIUM

Developer: LATERRE Antoine, BERNAERTS Valentine, QUOILIN Sylvain, MEYER Sébastien