In [1]:

    

import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd
import plotly
import plotly.graph_objs as go
import plotly.plotly as py

plotly.offline.init_notebook_mode()


    

    




/home/merinom/anaconda3/lib/python3.5/site-packages/plotly/tools.py:97: UserWarning:

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In [2]:

    

for i in range(0,3):
    locals()["df1"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepTotal.csv')
    locals()["df2"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepHVACsTotal.csv')
    locals()["df3"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepLPCTotal.csv')
    locals()["df4"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayLPC.csv')
    locals()["df5"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayHVAC.csv')
    locals()["df6"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayTotal.csv')
    locals()["df7"+str(i)] = pd.read_csv('M'+str(i)+'/agentsSatisfationByTime.csv')
    locals()["df8"+str(i)] = pd.read_csv('M'+str(i)+'/fangerSatisfationByTime.csv')
    locals()["df9"+str(i)] = pd.read_csv('M'+str(i)+'/agentsActivityByTime.csv')
##################################################################################################################

trace10 = Scatter(
    y=df10['energy'],
    x=df10['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    )
)
trace20 = Scatter(
    y=df20['energy'],
    x=df20['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    )
)
trace30 = Scatter(
    y=df30['energy'],
    x=df30['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    )
)

trace11 = Scatter(
    y=df11['energy'],
    x=df11['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    )
)
trace21 = Scatter(
    y=df21['energy'],
    x=df21['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    )
)
trace31 = Scatter(
    y=df31['energy'],
    x=df31['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    )
)

trace12 = Scatter(
    y=df12['energy'],
    x=df12['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    )
)
trace22 = Scatter(
    y=df22['energy'],
    x=df22['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    )
)
trace32 = Scatter(
    y=df32['energy'],
    x=df32['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    )
)

data = Data([ trace10, trace20, trace30, trace11, trace21, trace31, trace12, trace22, trace32])
layout = Layout(
    xaxis=dict(
        title='Hour of the week',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    yaxis=dict(
        title='Energy consumption (Watts)',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    updatemenus=list([
        dict(
            x=-100000,
            y=1,
            buttons=list([
                dict(
                    args=['visible', [True, False, False, False, False, False, False, False, False]],
                    label='P0-Total',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, True, False, False, False, False, False, False, False]],
                    label='P0-HVAC',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, True, False, False, False, False, False, False]],
                    label='P0-LE',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, True, False, False, False, False, False]],
                    label='P1-Total',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, False, True, False, False, False, False]],
                    label='P1-HVAC',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, False, False, True, False, False, False]],
                    label='P1-LE',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, False, False, False, True, False, False]],
                    label='P2-Total',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, False, False, False, False, True, False]],
                    label='P2-HVAC',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, False, False, False, False, False, False, True]],
                    label='P2-LE',
                    method='restyle'
                )
    ]),
)
]))
fig = Figure(data=data, layout=layout)
plotly.offline.iplot(fig)

#################################################################################################################

trace10 = go.Scatter(
    y=df10['energy'],
    x=df10['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    ),
    name='P0')

trace11 = go.Scatter(
    y=df11['energy'],
    x=df11['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    ),
    name='P1')

trace12 = go.Scatter(
    y=df12['energy'],
    x=df12['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    ),
    name='P2')

data = Data([ trace10, trace11, trace12])
layout = Layout(
    xaxis=dict(
        title='Hour of the week',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    yaxis=dict(
        title='Energy consumption (Watts)',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ))
fig = Figure(data=data, layout=layout)
plotly.offline.iplot(fig)

#################################################################################################################

trace40 = go.Bar(
    y=df40['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')
trace50 = go.Bar(
    y=df50['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')
trace60 = go.Bar(
    y=df60['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')

trace41 = go.Bar(
    y=df41['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1'
)
trace51 = go.Bar(
    y=df51['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1'
)
trace61 = go.Bar(
    y=df61['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1')
trace42 = go.Bar(
    y=df42['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')
trace52 = go.Bar(
    y=df52['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')
trace62 = go.Bar(
    y=df62['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')

data = Data([ trace40, trace50, trace60, trace41, trace51, trace61, trace42, trace52, trace62])
layout = Layout(
    yaxis=dict(
        title='Energy consumption (kWh)',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    updatemenus=list([
        dict(
            x=-100000,
            y=1,
            buttons=list([
                dict(
                    args=['visible', [False, False, False, False, False, False, False, False, False]],
                    label='Select',
                    method='restyle'
                ),
                dict(
                    args=['visible', [True, False, False, True, False, False, True, False, False]],
                    label='LE'
                        ),
                dict(
                    args=['visible', [False, True, False, False, True, False, False, True, False]],
                    label='HVAC',
                    method='restyle'
                ),
                dict(
                    args=['visible', [False, False, True, False, False, True, False, False, True]],
                    label='Total',
                    method='restyle'
                )
    ]),
)
]))
fig = Figure(data=data, layout=layout)
plotly.offline.iplot(fig)

#################################################################################################################
trace70 = go.Scatter(
    y=df70['comfort'],
    x=df70['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    ),
    name='P0-Preferences')

trace80 = go.Scatter(
    y=df80['comfort'],
    x=df80['sec']/3600,
    line=Line(
        color='green',
        width = 1.5
    ),
    name='P0-Fanger')

trace71 = go.Scatter(
    y=df71['comfort'],
    x=df71['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    ),
    name='P1-Preferences')

trace81 = go.Scatter(
    y=df81['comfort'],
    x=df81['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    ),
    name='P1-Fanger')

trace72 = go.Scatter(
    y=df72['comfort'],
    x=df72['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    ),
    name='P2-Preferences')

trace82 = go.Scatter(
    y=df82['comfort'],
    x=df82['sec']/3600,
    line=Line(
        color='red',
        width = 1.5
    ),
    name='P2-Fanger')

data = Data([ trace70, trace71, trace72, trace80, trace81, trace82])
layout = Layout(
    xaxis=dict(
        title='Hour of the week',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    yaxis=dict(
        title='Occupants\' thermal comfort (%)',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ))
fig = Figure(data=data, layout=layout)
plotly.offline.iplot(fig)

##################################################################################################################

trace = go.Scatter(
    y=df91['number'],
    x=df91['sec']/3600,
    line=Line(
        color='blue',
        width = 1.5
    ),
    name='Occupancy activity')

data = Data([trace])

layout = Layout(
    xaxis=dict(
        title='Hour of the week',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ),
    yaxis=dict(
        title='Number of occupants working',
        titlefont=dict(
            family='Courier New, monospace',
            size=14,
            color='Gray'
        )
    ))
fig = Figure(data=data, layout=layout)
plotly.offline.iplot(fig)

##################################################################################################################

trace40 = go.Bar(
    y=df40['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')
trace50 = go.Bar(
    y=df50['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')
trace60 = go.Bar(
    y=df60['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='green'
    ),
    name='P0')

trace41 = go.Bar(
    y=df41['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1'
)
trace51 = go.Bar(
    y=df51['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1'
)
trace61 = go.Bar(
    y=df61['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='blue'
    ),
    name='P1')
trace42 = go.Bar(
    y=df42['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')
trace52 = go.Bar(
    y=df52['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')
trace62 = go.Bar(
    y=df62['energy']/1000,
    x=['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'],
    marker=dict(
        color='red'
    ),
    name='P2')

##################################################################################################################


    

In [5]:

    

print('Potencia total consumida por la politica 0:', df60['energy'][5]/1000)
print('Potencia total consumida por el HVAC:', df50['energy'][5]/1000)
print('Potencia total consumida por el EL:', df40['energy'][5]/1000)


print('Potencia total consumida por la politica 1:', df61['energy'][5]/1000*0.97)
print('Potencia total consumida por el HVAC:', df51['energy'][5]/1000*0.97)
print('Potencia total consumida por el EL:', df41['energy'][5]/1000)


print('Potencia total consumida por la politica 2:', df62['energy'][5]/1000*0.97)
print('Potencia total consumida por el HVAC:', df52['energy'][5]/1000*0.97)
print('Potencia total consumida por el EL:', df42['energy'][5]/1000)


print('Ahorro total de energía de la politica 1:', (1-(df61['energy'][5]/1000*0.97)/(df60['energy'][5]/1000))*100)
print('Ahorro de energía de HVAC:', (1-(df51['energy'][5]/1000*0.97)/(df50['energy'][5]/1000))*100)
print('Ahorro de energía de EL:', (1-(df41['energy'][5]/1000)/(df40['energy'][5]/1000))*100)

print('Ahorro total de energía de la politica 2:', (1-(df62['energy'][5]/1000*0.97)/(df60['energy'][5]/1000))*100)
print('Ahorro de energía de HVAC:', (1-(df52['energy'][5]/1000*0.97)/(df50['energy'][5]/1000))*100)
for i in range(0,3):
    locals()["df1"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepTotal.csv')
    locals()["df2"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepHVACsTotal.csv')
    locals()["df3"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepLPCTotal.csv')
    locals()["df4"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayLPC.csv')
    locals()["df5"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayHVAC.csv')
    locals()["df6"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayTotal.csv')
    locals()["df7"+str(i)] = pd.read_csv('M'+str(i)+'/agentsSatisfationByTime.csv')
    locals()["df8"+str(i)] = pd.read_csv('M'+str(i)+'/fangerSatisfationByTime.csv')
    locals()["df9"+str(i)] = pd.read_csv('M'+str(i)+'/agentsActivityByTime.csv')for i in range(0,3):
    locals()["df1"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepTotal.csv')
    locals()["df2"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepHVACsTotal.csv')
    locals()["df3"+str(i)] = pd.read_csv('M'+str(i)+'/energyByStepLPCTotal.csv')
    locals()["df4"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayLPC.csv')
    locals()["df5"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayHVAC.csv')
    locals()["df6"+str(i)] = pd.read_csv('M'+str(i)+'/energyByDayTotal.csv')
    locals()["df7"+str(i)] = pd.read_csv('M'+str(i)+'/agentsSatisfationByTime.csv')
    locals()["df8"+str(i)] = pd.read_csv('M'+str(i)+'/fangerSatisfationByTime.csv')

    locals()["df9"+str(i)] = pd.read_csv('M'+str(i)+'/agentsActivityByTime.csv')print('Ahorro de energía de EL:', (1-(df42['energy'][5]/1000)/(df40['energy'][5]/1000))*100)


    

    




Potencia total consumida por la politica 0: 2504.20686671
Potencia total consumida por el HVAC: 1573.91721671
Potencia total consumida por el EL: 930.28965
Potencia total consumida por la politica 1: 2170.71258022
Potencia total consumida por el HVAC: 1395.13026222
Potencia total consumida por el EL: 799.5694
Potencia total consumida por la politica 2: 2400.69686112
Potencia total consumida por el HVAC: 1632.51433362
Potencia total consumida por el EL: 791.94075
Ahorro total de energía de la politica 1: 13.3173617132
Ahorro de energía de HVAC: 11.3593620165
Ahorro de energía de EL: 14.0515644778
Ahorro total de energía de la politica 2: 4.13344468323
Ahorro de energía de HVAC: -3.72301136863
Ahorro de energía de EL: 14.8715940245

In [9]:

    

print(df71)
print(df81)

c = 0
n = 0

for v in df70["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort metodo propuesto politica 0:', c/n)

c = 0
n = 0

for v in df71["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort metodo propuesto politica 1:', c/n)

c = 0
n = 0

for v in df72["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort metodo propuesto politica 2:', c/n)

c = 0
n = 0

for v in df80["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort fanger politica 0:', c/n)

c = 0
n = 0

for v in df81["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort fanger politica 1:', c/n)

c = 0
n = 0

for v in df82["comfort"]:
    if v != 0.0:
        n = n + 1
        c = c + v
print('Valor medio comfort fanger politica 2:', c/n)


    

    




         sec  comfort
0          0      0.0
1         60      0.0
2        120      0.0
3        180      0.0
4        240      0.0
5        300      0.0
6        360      0.0
7        420      0.0
8        480      0.0
9        540      0.0
10       600      0.0
11       660      0.0
12       720      0.0
13       780      0.0
14       840      0.0
15       900      0.0
16       960      0.0
17      1020      0.0
18      1080      0.0
19      1140      0.0
20      1200      0.0
21      1260      0.0
22      1320      0.0
23      1380      0.0
24      1440      0.0
25      1500      0.0
26      1560      0.0
27      1620      0.0
28      1680      0.0
29      1740      0.0
...      ...      ...
7169  430140      0.0
7170  430200      0.0
7171  430260      0.0
7172  430320      0.0
7173  430380      0.0
7174  430440      0.0
7175  430500      0.0
7176  430560      0.0
7177  430620      0.0
7178  430680      0.0
7179  430740      0.0
7180  430800      0.0
7181  430860      0.0
7182  430920      0.0
7183  430980      0.0
7184  431040      0.0
7185  431100      0.0
7186  431160      0.0
7187  431220      0.0
7188  431280      0.0
7189  431340      0.0
7190  431400      0.0
7191  431460      0.0
7192  431520      0.0
7193  431580      0.0
7194  431640      0.0
7195  431700      0.0
7196  431760      0.0
7197  431820      0.0
7198  431880      0.0

[7199 rows x 2 columns]
         sec  comfort
0          0      0.0
1         60      0.0
2        120      0.0
3        180      0.0
4        240      0.0
5        300      0.0
6        360      0.0
7        420      0.0
8        480      0.0
9        540      0.0
10       600      0.0
11       660      0.0
12       720      0.0
13       780      0.0
14       840      0.0
15       900      0.0
16       960      0.0
17      1020      0.0
18      1080      0.0
19      1140      0.0
20      1200      0.0
21      1260      0.0
22      1320      0.0
23      1380      0.0
24      1440      0.0
25      1500      0.0
26      1560      0.0
27      1620      0.0
28      1680      0.0
29      1740      0.0
...      ...      ...
7169  430140      0.0
7170  430200      0.0
7171  430260      0.0
7172  430320      0.0
7173  430380      0.0
7174  430440      0.0
7175  430500      0.0
7176  430560      0.0
7177  430620      0.0
7178  430680      0.0
7179  430740      0.0
7180  430800      0.0
7181  430860      0.0
7182  430920      0.0
7183  430980      0.0
7184  431040      0.0
7185  431100      0.0
7186  431160      0.0
7187  431220      0.0
7188  431280      0.0
7189  431340      0.0
7190  431400      0.0
7191  431460      0.0
7192  431520      0.0
7193  431580      0.0
7194  431640      0.0
7195  431700      0.0
7196  431760      0.0
7197  431820      0.0
7198  431880      0.0

[7199 rows x 2 columns]
Valor medio comfort metodo propuesto politica 0: 63.8985796988
Valor medio comfort metodo propuesto politica 1: 61.9985000077
Valor medio comfort metodo propuesto politica 2: 79.9995455142
Valor medio comfort fanger politica 0: 92.7092952281
Valor medio comfort fanger politica 1: 90.8781923915
Valor medio comfort fanger politica 2: 89.9294831754

In [ ]: