SwissHouseRSla notebook example

In this example, the usage of the model “SwissHouseRad-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("SwissHouseRSlaW2W-v0", weather=weather, simulation_days=20)

the initial variables are {'u': 0}

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

inputs = env.get_inputs_names()
outputs = env.get_outputs_names()
print("inputs:", inputs)
print("outputs:", outputs)

inputs: ['u']
outputs: ['TOut.T', 'heaPum.COP', 'heaPum.COPCar', 'heaPum.P', 'heaPum.QCon_flow', 'heaPum.QEva_flow', 'heaPum.TConAct', 'heaPum.TEvaAct', 'preHea.Q_flow', 'sla.QTot', 'sla.heatPortEmb[1].T', 'sla.m_flow', 'sunHea.Q_flow', 'sunRad.y', 'temRet.T', 'temRoo.T', 'temSup.T', 'weaBus.HDifHor', 'weaBus.HDirNor', 'weaBus.HGloHor', 'weaBus.HHorIR', 'y']

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*5
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	sla.QTot	...	sunRad.y	temRet.T	temRoo.T	temSup.T	weaBus.HDifHor	weaBus.HDirNor	weaBus.HGloHor	weaBus.HHorIR	y	time
0	273.040697	3.872714	13.583432	1000.0	3.872714e+03	-2.872714e+03	297.357658	275.466456	0.0	2937.284209	...	0.0	293.656276	293.153996	294.775662	0.0	0.0	0.0	198.356554	293.153996	300.0
1	273.050842	3.839777	13.467908	1000.0	3.839777e+03	-2.839777e+03	297.657343	275.556112	0.0	3503.467174	...	0.0	293.983947	293.163014	295.097367	0.0	0.0	0.0	198.314931	293.163014	600.0
2	273.060017	3.818085	13.391824	1000.0	3.818085e+03	-2.818085e+03	297.856813	275.615122	0.0	3868.495573	...	0.0	294.201873	293.175391	295.311340	0.0	0.0	0.0	198.276953	293.175391	900.0
3	273.067805	3.803545	13.340826	1000.0	3.803545e+03	-2.803545e+03	297.991447	275.654641	0.0	4103.549728	...	0.0	294.348908	293.189931	295.455694	0.0	0.0	0.0	198.244444	293.189931	1200.0
4	273.073788	3.793566	13.305825	1000.0	3.793566e+03	-2.793566e+03	298.084275	275.681735	0.0	4254.872673	...	0.0	294.450276	293.205857	295.555191	0.0	0.0	0.0	198.219227	293.205857	1500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1435	278.738889	5.361843	18.806509	0.0	2.407412e-35	-2.407412e-35	299.051489	283.150000	0.0	-0.000000	...	0.0	298.024250	296.434162	299.051481	0.0	0.0	0.0	298.691478	296.434162	430800.0
1436	278.737656	5.361843	18.806509	0.0	2.407412e-35	-2.407412e-35	299.051489	283.150000	0.0	-0.000000	...	0.0	298.024250	296.421192	299.051481	0.0	0.0	0.0	298.764373	296.421192	431100.0
1437	278.743194	5.361843	18.806509	0.0	2.407412e-35	-2.407412e-35	299.051489	283.150000	0.0	-0.000000	...	0.0	298.024250	296.408233	299.051481	0.0	0.0	0.0	298.817684	296.408233	431400.0
1438	278.756858	5.361843	18.806509	0.0	2.407412e-35	-2.407412e-35	299.051489	283.150000	0.0	-0.000000	...	0.0	298.024250	296.395291	299.051481	0.0	0.0	0.0	298.847523	296.395291	431700.0
1439	278.780000	5.361843	18.806509	0.0	2.407412e-35	-2.407412e-35	299.051489	283.150000	0.0	-0.000000	...	0.0	298.024250	296.382373	299.051481	0.0	0.0	0.0	298.850000	296.382373	432000.0

1440 rows × 23 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['sla.heatPortEmb[1].T']-273.15, 'b--')
ax1.plot(out_df['heaPum.TEvaAct']-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()

SwissHouseRSla SwissHouseRSlaTank