parametric_simulation_usage_v00_accim_predef.py

import accim
from accim.parametric_and_optimisation.objectives import return_time_series
from besos import eppy_funcs as ef
from matplotlib import pyplot as plt
from accim.utils import print_available_outputs_mod
from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file

# 1. check output data
# 2. check input dataframe
# 3. run parametric_and_optimisation simulation




#Arguments
idf_path = 'TestModel.idf'

building = ef.get_building(idf_path)

accim.utils.set_occupancy_to_always(idf_object=building)


parametric = OptimParamSimulation(
    building=building,
    parameters_type='accim predefined model'
    # output_keep_existing=False,
    # debugging=True

)

# Setting the Output:Variable and Output:Meter objects in the idf
df_output_variables_idf = parametric.get_output_var_df_from_idf()

df_output_variables_idf_mod = df_output_variables_idf.copy()

[i for i in df_output_variables_idf['variable_name'] if 'Running Average Outdoor' in i]

df_output_variables_idf_mod = df_output_variables_idf_mod[
    (
        df_output_variables_idf_mod['variable_name'].str.contains('Setpoint Temperature_No Tolerance')
        |
        df_output_variables_idf_mod['variable_name'].str.contains('Zone Operative Temperature')
        |
        df_output_variables_idf_mod['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')
    )
]

[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']

parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf_mod)

df_output_meters_idf = parametric.get_output_meter_df_from_idf()

output_meters = [
    'Heating:Electricity',
    'Cooling:Electricity',
    'Electricity:HVAC',
]
parametric.set_output_met_objects_to_idf(output_meters=output_meters)

# Checking the Output:Meter and Output:Variable objects in the simulation
df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()

#Other variables could be reported. These can be read in the rdd, mdd and mtd files
df_rdd = get_rdd_file_as_df()
df_mdd = get_mdd_file_as_df()
meter_list = parse_mtd_file()


# To end with outputs, let's set the objective outputs (outputs for the Problem object), which are those displayed by BESOS in case of parametric_and_optimisation analysis, or used in case of optimisation

# def average_results(result):
#     return result.data["Value"].mean()
# def sum_results(result):
#     return result.data["Value"].sum()
#
# def return_time_series(result):
#     return result.data["Value"].to_list()

# df_outputmeters_3 = df_outputmeters_2.copy()
# df_outputvariables_3 = df_outputvariables_2.copy()

df_output_variables_testsim['func'] = return_time_series
df_output_variables_testsim = df_output_variables_testsim.drop(index=[2, 4])
df_output_variables_testsim['name'] = df_output_variables_testsim['variable_name'] + '_time series'

parametric.set_outputs_for_simulation(
    df_output_meter=df_output_meters_testsim,
    df_output_variable=df_output_variables_testsim,
)

# At this point, the outputs of each energyplus simulation has been set. So, next step is setting parameters


# accis.modifyAccis(
#     idf=building,
#     ComfStand=99,
#     ComfMod=3,
#     CAT=80,
#     HVACmode=2,
#     VentCtrl=0,
# )


# accis_parameters = {
#     'CustAST_m': (0.01, 0.99),
#     'CustAST_n': (5, 23),
#     'CustAST_ASToffset': (2, 4),
#     'CustAST_ASTall': (10, 15),
#     'CustAST_ASTaul': (30, 35),
# }


accis_parameters = {
    'ComfStand': [1, 2],
    'CAT': [80, 90],
    'ComfMod': [3],
}


# from besos.parameters import wwr, RangeParameter
# other_parameters = [wwr(RangeParameter(0.1, 0.9))]

parametric.set_parameters(
    accis_params_dict=accis_parameters,
    # additional_params=other_parameters
)

# Let's set the problem
parametric.set_problem()

# Let's generate a sampling dataframe
# parametric.sampling_full_factorial(level=5)
# temp_full_fac = parametric.parameters_values_df

# parametric.sampling_lhs(num_samples=3)
# temp_lhs = parametric.parameters_values_df

parametric.sampling_full_set()
temp_full_set = parametric.parameters_values_df

parametric.run_parametric_simulation(
    epws=[
        'Sydney.epw',
        'Seville.epw'
    ],
    out_dir='WIP_testing predefined models',
    df=temp_full_set,
    processes=6,
)

parametric.get_hourly_df()
# outputs = outputs.reset_index()

# outputs.to_excel('WIP_outputs.xlsx')

[i.name for i in parametric.sim_outputs]
##


import seaborn as sns
import ast
rmot = [i for i in outputs.columns if 'Running' in i][0]
optemp = [i for i in outputs.columns if 'Operative' in i][0]
ahst = [i for i in outputs.columns if 'Adaptive Heating' in i][0]
acst = [i for i in outputs.columns if 'Adaptive Cooling' in i][0]

# sns.scatterplot(
#     x=[float(i) for i in outputs.loc[1][rmot]],
#     y=[float(i) for i in outputs.loc[1][optemp]]
# )

fig, axs = plt.subplots(
    nrows=len(outputs),
    figsize=(10, 5)
)
for i in outputs.index:
    for c in [optemp, acst, ahst]:
        x = ast.literal_eval(outputs.loc[i, rmot])
        y = ast.literal_eval(outputs.loc[i, c])

        sns.scatterplot(
            x=x,
            y=y,
            ax=axs[i]
        )




##

#Let's make a copy of the dataframe to not to modify the original one
df = parametric.outputs_param_simulation_hourly.copy()

# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:
rmot = [i for i in df.columns if 'Running Average' in i][0]

#Let's remove the columns where value is the same for all rows
for c in df.columns:
    if len(set(df[c])) == 1:
        df = df.drop(columns=[c])
#Now let's remove the hour and datetime columns, since will
df = df.drop(columns=['hour'])

# Now let's reshape the df for plotting purposes
df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])


##
import seaborn as sns

g = sns.FacetGrid(
    data=df,
    row='CAT',
    col='epw'
)
g.map_dataframe(
    sns.scatterplot,
    x=rmot,
    y='value',
    hue='variable',
    s=1,
    alpha=0.5
)
g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')
g.add_legend(loc='lower center')

for lh in g._legend.legend_handles:
    lh.set_markersize(5)

# handles, lables = g.get_legend_handles_labels()
# for h in handles:
#     h.set_markersize(10)

# plt.legend(
#     # loc=[1.01,1.01],
#     prop={'size': 13},
#     markerscale=2
# )

plt.tight_layout()


##
import seaborn as sns
g = sns.FacetGrid(
    data=df,
    row='CAT',
    col='epw'
)
g.map_dataframe(
    sns.lineplot,
    x='datetime',
    y='value',
    hue='variable',
    # s=1,
    # alpha=0.5
)
g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')
g.add_legend(loc='lower center')
plt.tight_layout()

# for lh in g._legend.legend_handles:
#     lh.set_markersize(5)


##

import pandas as pd
import ast
from datetime import datetime, timedelta


##



##
# remove_accents_in_idf(idf_path=idf_path)

# gv = [i for i in building.idfobjects['EnergyManagementSystem:GlobalVariable']]
# [i.Variable_Name for i in building.idfobjects['output:variable'] if 'Occupied Discomfortable' in i.Variable_Name]











# Objectives






# obj_avg = [MeterReader(key_name='TOTAL OCCUPIED DISCOMFORTABLE HOURS', func=avg, name='AVERAGE OCCUPIED DISCOMFORTABLE HOURS')]




##