Brussels. Step 2.b Dynamic Sampling Model and GREGWT, Non-Residential Model¶

In [1]:

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

2018-04-03 15:12:00.614979

Notebook abstract

This notebook shows the main sampling and reweighting algorithm for the non-residential sector.

Import libraries¶

In [2]:

from smum.microsim.run import run_calibrated_model
from smum.microsim.util_plot import plot_data_projection
from smum.microsim.table import TableModel

/usr/lib/python3.6/site-packages/h5py-2.7.1-py3.6-linux-x86_64.egg/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

Global variables¶

In [3]:

iterations = 10000
benchmark_year = 2016
census_file = 'data/benchmarks_nonresidential.csv'
typ = 'resampled'
model_name = 'Brussels_NonResidentialElectricity_wbias_projected_dynamic_{}'.format(typ)
verbose = False
drop_col_survey = ['h_BuildingHeat', 'c_BuldingCool', 'e_BuildingElec',
                   'h_BuildingSqm', 'c_BuildingSqm']

Define model¶

In [4]:

tm = TableModel(census_file = census_file, verbose=verbose)
table_model_name = 'data/table_elec_nr_{}.csv'

Electricity¶

In [5]:

tm.add_model(table_model_name.format('e'), 'elec', static = True)
tm.add_model(table_model_name.format('h'), 'heat', static = True)
tm.add_model(table_model_name.format('c'), 'cool', static = True)

In [6]:

tm.models['cool'].loc[2020]

Out[6]:

	co_mu	co_sd	p	dis	lb	ub
c_BuildingSqm	3600.0,525.0,11100.0,12000.0,4650.0,105.0,105....	36.0,5.25,111.0,120.0,46.5,1.05,1.05,0.39,1.05...	0.00383471907281,0.00152179390275,0.0001305114...	Deterministic;c;Categorical	0	inf
c_BuildingCool	42.9002267754,125.188571429,23.4353153153,31.9...	0.429002267754,1.25188571429,0.234353153153,0....	0.00383471907281,0.00152179390275,0.0001305114...	Deterministic;BuildingSqm;Categorical	0	inf

In [7]:

formula_nrb = "c_e_BuildingElec * e_BuildingElec * c_e_BuildingSqm * e_BuildingSqm"
tm.add_formula(formula_nrb, 'elec')

In [8]:

formula_nrb = "c_h_BuildingHeat * h_BuildingHeat * c_e_BuildingSqm * e_BuildingSqm"
tm.add_formula(formula_nrb, 'heat')

In [9]:

formula_nrb = "c_c_BuildingCool * c_BuildingCool * c_e_BuildingSqm * e_BuildingSqm"
tm.add_formula(formula_nrb, 'cool')

In [10]:

table_model = tm.make_model()
tm.to_excel()

creating data/tableModel_elec.xlsx
creating data/tableModel_heat.xlsx
creating data/tableModel_cool.xlsx

Run model¶

In [ ]:

fw = run_calibrated_model(
    table_model,
    verbose = verbose,
    project = typ,
    census_file = census_file,
    year = benchmark_year,
    population_size = False,
    name = '{}_{}'.format(model_name, iterations),
    iterations = iterations,
    align_census = False,
    drop_col_survey = drop_col_survey
)

Plot results¶

In [12]:

reweighted_survey = 'data/survey_{}_{}'.format(model_name, iterations)

In [13]:

data = plot_data_projection(
    reweighted_survey, ['elec','heat','cool'], "{}, {}".format(iterations, typ),
    start_year = 2016, end_year = 2025, aspect_ratio = 4,
    benchmark_year = False, unit = "building")

../../_images/example_be_Bb_GREGWT_NonResidential_20_0.png