Brussels. Step 3.b. Visualize transition scenarios

import datetime; print(datetime.datetime.now())

2018-04-09 11:47:45.683429

Notebook Abstract:

The following notebook visualize the the simple transition scenarios by plotting the total consumption over all simulation years and the per-capita consumption rate. Depending on the define scenarios the per-capita consumption rate can be maintained constant. The per-capita consumption value is computed as total consumption divided by population size.

Import libraries

from smum.microsim.util_plot import plot_data_projection

The visualization is performed with help of the module function plot_data_projection().

Global variables

iterations = 1000
typ = 'resampled'
model_name = "Brussels_Electricity_Water_projected_dynamic_{}_bias".format(typ)
reweighted_survey = 'data/survey_{}_{}'.format(model_name, iterations)

Base scenario

var = ['Water', 'Electricity']
data = plot_data_projection(
    reweighted_survey, var, "{}, {}".format(iterations, typ),
    benchmark_year=False, start_year=2016, end_year=2025
)

Base scenario grouped by construction type

var = ['Water', 'Electricity']
groupby = 'w_ConstructionType'
data = plot_data_projection(
    reweighted_survey, var, "{}, {} by {}".format(iterations, typ, groupby),
    benchmark_year = False, start_year=2016, end_year=2025,
    groupby = groupby
)

Base scenario grouped by construction year

var = ['Water', 'Electricity']
groupby = 'w_ConstructionYear'
data = plot_data_projection(
    reweighted_survey, var, "{}, {} by {}".format(iterations, typ, groupby),
    benchmark_year = False, start_year=2016, end_year=2025,
    groupby = groupby
)

Scenario 1 compared to base scenario

import numpy as np
pr = [i for i in np.linspace(0, 0.3, num=10)]
scenario_name = 'scenario 1'

variables = ['Water', 'Electricity']
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {}, alt. scenario 1".format(iterations, typ),
        benchmark_year=False,  start_year=2016, end_year=2025,
        pr = pr, scenario_name = scenario_name,
        aspect_ratio = 2,
    )

Scenario 1 grouped by construction year

variables = ['Water', 'Electricity']
groupby = 'w_ConstructionYear'
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {} by {}, alt. scenario 1".format(iterations, typ, groupby),
        benchmark_year=False,  start_year=2016, end_year=2025,
        pr = pr, scenario_name = scenario_name,
        groupby = groupby,
        aspect_ratio = 2,
    )

import numpy as np
pr = [i for i in np.linspace(0, 0.6, num=10)]
scenario_name = 'scenario 2'

Scenario 2 compared to base scenario

variables = ['Water', 'Electricity']
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {}, alt. scenario 2".format(iterations, typ),
        benchmark_year=False, start_year=2016, end_year=2025,
        pr = pr, scenario_name = scenario_name,
        aspect_ratio = 2,
    )

Scenario 2 grouped by construction year

variables = ['Water', 'Electricity']
groupby = 'w_ConstructionYear'
for var in variables:
    var = [var]
    data = plot_data_projection(
        reweighted_survey, var, "{}, {} by {}, alt. scenario 2".format(iterations, typ, groupby),
        benchmark_year=False, start_year=2016, end_year=2025,
        pr = pr, scenario_name = scenario_name,
        groupby = groupby,
        aspect_ratio = 2,
    )

Scenario 2 grouped by construction year, cross tabulation

from smum.microsim.util_plot import cross_tab

a = 'Water'
b = 'w_ConstructionYear'
ct = cross_tab(a, b, 2025, reweighted_survey + "_{}_scenario 2_0.60.csv", split_a = True)

data saved as: data/Water_w_ConstructionYear_2025.xlsx

ct

w_ConstructionYear	ConstructionYear_1900	ConstructionYear_1918	ConstructionYear_1945	ConstructionYear_1961	ConstructionYear_1970	ConstructionYear_1981	ConstructionYear_1991	ConstructionYear_2001	ConstructionYear_2011	ConstructionYear_2016	ConstructionYear_2020	ConstructionYear_2030	ConstructionYear_2035
Water
Low	756.00	2107.00	31728.00	24695.00	20596.00	19762.00	41657.00	36786.00	37786.00	15789.00	17284.00	27518.00	27313.31
mid-Low	3783.00	NaN	23941.59	33933.60	12107.27	21273.51	27826.15	25649.13	36460.21	15256.25	11792.58	25899.59	14275.55
Middle	5432.62	38.32	15585.17	21938.42	11685.87	25276.22	16113.10	30999.13	38580.44	26178.14	16685.87	32066.15	15668.16
mid-High	910.64	NaN	17586.04	23999.04	10350.21	20567.05	33363.96	31628.68	19203.35	14848.76	12146.85	18881.18	10398.89
High	1989.63	NaN	14134.40	13426.42	20346.68	22532.30	24775.92	35835.83	31014.19	13740.42	6450.16	24354.21	7430.19

a = 'Electricity'
b = 'w_ConstructionYear'
ct = cross_tab(a, b, 2025, reweighted_survey + "_{}_scenario 2_0.60.csv", split_a = True)

data saved as: data/Electricity_w_ConstructionYear_2025.xlsx

ct

w_ConstructionYear	ConstructionYear_1900	ConstructionYear_1918	ConstructionYear_1945	ConstructionYear_1961	ConstructionYear_1970	ConstructionYear_1981	ConstructionYear_1991	ConstructionYear_2001	ConstructionYear_2011	ConstructionYear_2016	ConstructionYear_2020	ConstructionYear_2030	ConstructionYear_2035
Electricity
Low	NaN	NaN	33418.00	29831.00	16378.00	24107.00	34345.00	38718.00	44712.00	15356.31	13317.00	39565.00	22970.00
mid-Low	2656.00	2107.00	16546.00	34296.00	18102.00	26041.00	25023.00	39102.00	42224.00	21567.00	20670.00	34543.00	14090.00
Middle	4184.00	NaN	25195.41	21668.80	12685.21	17660.15	22405.50	27688.46	27039.38	17181.52	4476.64	20975.16	13303.24
mid-High	2015.63	NaN	10714.45	15683.31	12347.43	25470.56	24511.52	27061.47	23394.93	13715.08	19022.35	13591.65	17715.70
High	4016.26	38.32	17101.34	16513.37	15573.39	16132.37	37451.12	28328.85	25673.88	17992.67	6873.47	20044.31	7007.16