SimpleHouseRSla notebook example

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

the initial variables are {'u': 0.0}

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	sla.QTot	...	sunRad.y	temRet.T	temRoo.T	temSup.T	weaBus.HDifHor	weaBus.HDirNor	weaBus.HGloHor	weaBus.HHorIR	y	time
0	273.040697	4.845457	10.197181	5000.0	2.422729e+04	-1.922729e+04	306.383478	276.337580	0.0	11709.697791	...	0.0	299.165850	293.110455	304.431161	0.0	0.0	0.0	198.356554	293.110455	300.0
1	273.050842	4.497996	9.465954	5000.0	2.248998e+04	-1.748998e+04	309.695190	276.978448	0.0	15166.910884	...	0.0	302.821032	293.214613	307.887631	0.0	0.0	0.0	198.314931	293.214613	600.0
2	273.060017	4.309981	9.070281	5000.0	2.154991e+04	-1.654991e+04	311.686527	277.323036	0.0	17116.428223	...	0.0	304.992757	293.394047	309.957447	0.0	0.0	0.0	198.276953	293.394047	900.0
3	273.067805	4.200541	8.839966	5000.0	2.100270e+04	-1.600270e+04	312.921007	277.522566	0.0	18209.589180	...	0.0	306.333322	293.614137	311.237577	0.0	0.0	0.0	198.244444	293.614137	1200.0
4	273.073788	4.132531	8.696839	5000.0	2.066265e+04	-1.566265e+04	313.718667	277.645945	0.0	18812.735306	...	0.0	307.199889	293.855164	312.063525	0.0	0.0	0.0	198.219227	293.855164	1500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2875	278.540000	4.120141	8.670766	0.0	2.350989e-38	-2.350989e-38	320.062872	283.150000	0.0	0.000000	...	0.0	315.810929	297.693424	320.062482	0.0	0.0	0.0	236.600000	297.693424	862800.0
2876	278.540000	4.120141	8.670766	0.0	2.350989e-38	-2.350989e-38	320.062872	283.150000	0.0	0.000000	...	0.0	315.810929	297.414317	320.062482	0.0	0.0	0.0	236.962500	297.414317	863100.0
2877	278.540000	4.120141	8.670766	0.0	2.350989e-38	-2.350989e-38	320.062872	283.150000	0.0	0.000000	...	0.0	315.810929	297.139277	320.062482	0.0	0.0	0.0	237.325000	297.139277	863400.0
2878	278.540000	4.120141	8.670766	0.0	2.350989e-38	-2.350989e-38	320.062872	283.150000	0.0	0.000000	...	0.0	315.810929	296.868245	320.062482	0.0	0.0	0.0	237.687500	296.868245	863700.0
2879	278.540000	4.120141	8.670766	0.0	2.350989e-38	-2.350989e-38	320.062872	283.150000	0.0	0.000000	...	0.0	315.810929	296.601162	320.062482	0.0	0.0	0.0	238.050000	296.601162	864000.0

2880 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['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()

SimpleHouseRSla SwissHouseRSla