SimpleHouseRad notebook example

In this example, the usage of the model “SimpleHouseRad-v0” is demonstrated.

First, we import Energym and create the simulation environment by specifying the model, a weather file and the number of simulation days.

import energym

weather = "CH_BS_Basel"
env = energym.make("SimpleHouseRad-v0", weather=weather, simulation_days=20)

The control inputs can be inspected using the get_inputs_names() method.

inputs = env.get_inputs_names()
print(inputs)

['u']

To run the simulation, a number of steps is specified (here 288 steps per day for 10 days), a control input is specified and passed to the simulation model with the step() method. To generate some plots later on, we save all the outputs in lists.

from scipy import signal

steps = 288*10
out_list = []
outputs = env.get_output()
controls = []
hour = 0
for i in range(steps):
    control = {}
    control['u'] = [0.5*(signal.square(0.1*i)+1.0)]
    controls +=[ {p:control[p][0] for p in control} ]
    outputs = env.step(control)
    _,hour,_,_ = env.get_date()
    out_list.append(outputs)

[OK] %s
[WARNING] %s

Since the outputs are given as dictionaries and are collected in lists, we can simply load them as a pandas.DataFrame.

import pandas as pd
out_df = pd.DataFrame(out_list)

out_df

	TOut.T	heaPum.COP	heaPum.COPCar	heaPum.P	heaPum.QCon_flow	heaPum.QEva_flow	heaPum.TConAct	heaPum.TEvaAct	preHea.Q_flow	rad.Q_flow	...	sunRad.y	temRet.T	temRoo.T	temSup.T	weaBus.HDifHor	weaBus.HDirNor	weaBus.HGloHor	weaBus.HHorIR	y	time
0	273.040697	3.534267	7.437804	5000.0	1.767134e+04	-1.267134e+04	321.988855	278.698008	0.0	-17920.641869	...	0.0	316.440754	293.390546	320.572635	0.0	0.0	0.0	198.356554	293.390546	300.0
1	273.050842	3.534640	7.438588	5000.0	1.767320e+04	-1.267320e+04	321.988496	278.702258	0.0	-17681.274614	...	0.0	316.348049	293.612518	320.574636	0.0	0.0	0.0	198.314931	293.612518	600.0
2	273.060017	3.533138	7.435427	5000.0	1.766569e+04	-1.266569e+04	322.012420	278.704562	0.0	-17516.873945	...	0.0	316.379873	293.825719	320.599010	0.0	0.0	0.0	198.276953	293.825719	900.0
3	273.067805	3.529085	7.426898	5000.0	1.764542e+04	-1.264542e+04	322.077806	278.711408	0.0	-17396.958556	...	0.0	316.455622	294.031841	320.665911	0.0	0.0	0.0	198.244444	294.031841	1200.0
4	273.073788	3.523458	7.415057	5000.0	1.761729e+04	-1.261729e+04	322.169078	278.721120	0.0	-17306.466774	...	0.0	316.558271	294.232023	320.759368	0.0	0.0	0.0	198.219227	294.232023	1500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2875	278.540000	3.641306	7.663066	0.0	-1.323489e-23	1.323489e-23	325.645451	283.150000	0.0	-840.945080	...	0.0	321.767688	297.376704	325.645098	0.0	0.0	0.0	236.600000	297.376704	862800.0
2876	278.540000	3.641306	7.663066	0.0	-1.323489e-23	1.323489e-23	325.645451	283.150000	0.0	-801.500216	...	0.0	321.767688	297.126147	325.645098	0.0	0.0	0.0	236.962500	297.126147	863100.0
2877	278.540000	3.641306	7.663066	0.0	-1.323489e-23	1.323489e-23	325.645451	283.150000	0.0	-768.308001	...	0.0	321.767688	296.878184	325.645098	0.0	0.0	0.0	237.325000	296.878184	863400.0
2878	278.540000	3.641306	7.663066	0.0	-1.323489e-23	1.323489e-23	325.645451	283.150000	0.0	-740.042078	...	0.0	321.767688	296.632943	325.645098	0.0	0.0	0.0	237.687500	296.632943	863700.0
2879	278.540000	3.641306	7.663066	0.0	-1.323489e-23	1.323489e-23	325.645451	283.150000	0.0	-715.733685	...	0.0	321.767688	296.390511	325.645098	0.0	0.0	0.0	238.050000	296.390511	864000.0

2880 rows × 22 columns

To generate plots, we can directly get the data from the DataFrames, by using the key names. Displayed are the room temperature, the supply temperature and the return temperature, as well as the external temperature, and the heat pump energy.

import matplotlib.pyplot as plt
%matplotlib notebook

f, (ax1,ax2,ax3) = plt.subplots(3,figsize=(10,15))#


ax1.plot(out_df['temRoo.T']-273.15, 'r')
ax1.plot(out_df['temSup.T']-273.15, 'b--')
ax1.plot(out_df['temRet.T']-273.15, 'orange')
ax1.set_ylabel('Temp')
ax1.set_xlabel('Steps')

ax2.plot(out_df['TOut.T']-273.15, 'r')
ax2.set_ylabel('Temp')
ax2.set_xlabel('Steps')

ax3.plot(out_df['heaPum.QCon_flow'], 'g')
ax3.set_ylabel('Energy')
ax3.set_xlabel('Steps')

plt.subplots_adjust(hspace=0.4)

plt.show()

To end the simulation, the close() method is called. It deletes files that were produced during the simulation and stores some information about the simulation in the energym_runs folder.

env.close()

Folder could not be removed.

SimpleHouseRad SimpleHouseRSla