Brussels. Step 3.b.2 Visualize transition scenarios NR

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

2018-04-09 10:53:30.322370

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 = 10000
typ = 'resampled'
model_name = "Brussels_NonResidentialElectricity_wbias_projected_dynamic_{}".format(typ)
reweighted_survey = 'data/survey_{}_{}'.format(model_name, iterations)

Base scenario

var = ['elec', 'heat', 'cool']
data = plot_data_projection(
    reweighted_survey, var, "{}, {}".format(iterations, typ),
    benchmark_year=2016, start_year=2016, end_year=2025
)

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 = ['elec', 'heat', 'cool']
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 2 compared to base scenario

variables = ['elec', 'heat', 'cool']
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,
    )