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4_analyze_predicted_occ.py
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4_analyze_predicted_occ.py
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"""
This script analyzes the MPC results and generates plots in
"results/figs"
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Zones
#zones = ['22-511-2', '20-601b-2']
zones=['20-601b-2']
for zone in zones:
# MPC results directory
res = os.path.join('new_results', 'mpc_predicted_occ_v2','600_qr_200', zone)
# Output directory
out = os.path.join('new_results', 'figs_predicted_occ_v2','600_qr_200', zone)
if not os.path.exists(out):
os.makedirs(out)
# Read data
tindex = pd.read_csv(os.path.join(res, 'tindex.csv'))
tindex['datetime'] = pd.to_datetime(tindex['datetime'])
tindex['time'] /= 3600.
t = tindex['time'].values
constr = pd.read_csv(os.path.join(res, 'constr.csv'))
u = pd.read_csv(os.path.join(res, 'u.csv'))
xctr = pd.read_csv(os.path.join(res, 'xctr.csv'))
xemu = pd.read_csv(os.path.join(res, 'xemu.csv'))
yemu = pd.read_csv(os.path.join(res, 'yemu.csv'))
#plotting indoor temperature vs. constraints
plt.figure(figsize=(10,4),dpi=150)
plt.plot(constr['Tair_lo'], color='black', ls='--', lw=1.0)
plt.plot(constr['Tair_hi'], color='black', ls='--', lw=1.0)
plt.plot(yemu['T'],color='r', label='Indoor temperature')
plt.legend()
plt.title('Indoor temperature vs. constraints')
plt.xticks(np.arange(0,t[-1],24))
plt.ylabel('T[K]')
plt.xlabel('t[h]')
plt.savefig(os.path.join(out,'T.png'))
#plotting valve position of heating/cooling supply
plt.figure(figsize=(10,4),dpi=150)
plt.plot(u['vpos'],color='b', label='Valve position')
plt.legend()
plt.title('Valve positon')
plt.xticks(np.arange(0,t[-1],24))
plt.ylabel('vpos[%]')
plt.xlabel('t[h]')
plt.savefig(os.path.join(out,'vpos.png'))
#plot energy consumption
plt.figure(figsize=(10,4),dpi=150)
plt.plot(yemu['qr']/1000.,color=[1,0.5,0.5], label='Energy consumption')
plt.legend()
plt.title('Energy consumption')
plt.xticks(np.arange(0,t[-1],24))
plt.ylabel('q[KW]')
plt.xlabel('t[h]')
plt.savefig(os.path.join(out,'q.png'))
q_total=(yemu['qr'].abs()/1000.).sum()
Q=pd.DataFrame({'Q':[q_total]})
Q.to_csv(os.path.join(out,'Q.csv'))
##plot occupancy of two zones
#mea_601b=pd.read_csv(os.path.join('new_results','mpc_ideal_occ','240_qr','20-601b-2','meas.csv'))
#mea_511=pd.read_csv(os.path.join('new_results','mpc_ideal_occ','240_qr','22-511-2','meas.csv'))
#plt.figure(figsize=(10,4),dpi=150)
#plt.plot(mea_601b['occ'],color='r', label='20-601b-2')
#plt.plot(mea_511['occ'],color='b', label='22-511-2')
#plt.legend()
#plt.title('occupancy of two zones')
#plt.xticks(np.arange(0,t[-1],24))
#plt.ylabel('occupancy')
#plt.xlabel('t[h]')
#plt.savefig(os.path.join('new_results','figs_ideal_occ','occ.png'))