3.1 Users’ behavior and energy consumption patterns
Energy consumption pattern and its relation with users’ behavior, comfort and environmental factors was investigated to uncover energy saving potential. Unlike residential buildings, where behavior of inhabitants is not conditioned by strict time schedules, occupation of office buildings usually is a result of well-defined timetables. However, number of occupants variates along the day. Thus, occupation pattern and its relation with energy consumption has been examined on the example of the office lab. As figure 3 demonstrates, the number of users increases from around 9:00 am and reaches its peak just before the midday. Then, around 1:00 – 2:00 pm it decreases, as a result of a lunchtime brake, and it rises slightly again in the early afternoon, to go down completely around 6:00 pm when users leave for home. This pattern is very similar for all weekdays. In addition, on Monday and Wednesday some minor activity can be observed before 9:00 am caused by the cleaning service who maintains the lab twice a week.
A strong correlation between users’ behavior and energy consumption can be noticed since both curves are relatively parallel. Nevertheless, a significant use of energy is observed even in periods with no users’ activity: evenings, nights and weekends. This is due to the consumption rendered by three servers and some workstations executing computation tasks which are turned on constantly. The total energy consumption rendered by users of the office lab, during two four-week periods: summer 2019 and winter 2020, has been summarized in table 1.
Table 1 Comparison of energy consumption in summer and winter periods.
|
Summer 2019
|
Winter 2020
|
|
kWh
|
%
|
kWh
|
%
|
Total energy consumption
|
543.2
|
100
|
555.2
|
100
|
Energy consumption working hours[1]
|
259.0
|
48
|
216.8
|
39
|
Energy consumption afterhours[2]
|
284.2
|
52
|
338.3
|
61
|
This clearly demonstrates that in spite of unquestionable correlation between energy use and occupation, the majority of energy consumption (52% in the summer period and 61% in the winter period) was rendered after hours when no users’ activity was registered.
To get a better picture and identify the sources of major consumption, energy demand of workstations was compared for the summer and winter periods in figure 4.
As figure 4 shows, workstations 1, 5, 11, 12, 13, 15 and 16 turned out to be the most energy intensive with consumption exceeding the mean value. The distribution of appliances in the lab shown in figure 1 helps to understand this result. Workstation 1 in the summer period was shared with a server, which was removed latter. This explains significantly lower energy consumption in the winter. On workstation 5, a very powerful machine was used to process complex tasks which resulted in higher consumption than those of the other workstations. Occasionally some other devices were plugged in too, which impacted the total consumption. The same applies to the workstation 12 in the winter period. Workstation 11 was turned on almost all the time in the summer period and turned off for the weekends in the winter, which explains the difference in consumption between these two periods. Number 13 encompasses two workstations located in close proximity. And workstations 15 and 16 were shared with servers.
3.2. Energy efficiency indicators
In order to obtain a deeper insight and measure the energy efficiency, a set of indicators was used. The most common one correlates energy consumption with space area (kWh/m2). This indicator refers to technical properties of the building and as it was argued by Huovila et al. (2017) it may be useful at the design and planning stage; however, it omits the actual user activity which is crucial in building operation phase. Users’ activity has a tremendous impact on building energy performance which may lead to a considerable discrepancy between the predicted and actual energy consumption of buildings (Yang, Santamouris and Lee, 2016; Calì et al., 2016). For this reason, understanding of the human activity and the interplay of building occupancy and energy consumption is essential. This is why kWh/m2 indicator was complemented with others, that consider human factor: 1) kWh/person which correlates use of energy with the number of occupants; 2) kWh/person hours, which correlates the energy consumption with the actual sum of the number of hours that users spend in the building, or its specific area, during the given period of time. For the sake of this study, periods of four weeks were examined; 3) kWh/m2 person hours, which combines indicators 1) and 2), to take into account floor area as well as its occupation time according to the formula:
Energy per area per occupied hours = , where kWh is the amount of energy consumed during the given period of time, m2 is the floor area of the space in question, and h is the number of hours the space was occupied during the given period (Dooley, 2011).
The performance of the office lab according to these four indicators was compared in table 2.
Table 2 comparison of energy indicators of the office lab in the summer and winter periods.
|
m2
|
No. of users
|
kWh
|
m2/ person
|
kWh/ m2
|
kWh/ workstation
|
kWh/ person
|
kWh/ person hours
|
kWh/ m2, person hours
|
Summer
|
72
|
16
|
543.2
|
4.5
|
7.5
|
28.2
|
34.0
|
3.1
|
43
|
Winter
|
14
|
555.2
|
5.1
|
7.7
|
29.3
|
39.7
|
3.9
|
54
|
Perversely, in spite of a greater number of users, less energy was consumed in the summer period. Spanish norm for occupation defines the standard of minimum 5 m2 per person for this kind of space (Ministerio de Fomento, 2019), for which in the summer period the office lab was slightly overused with only 4.5 m2/person. This translated into higher energy efficiency shown by other indicators. It is worth to stress that the difference in these two periods is especially seen in the case of indicators that involve human factor.
3.3. Covid-19 lock down and post lock down impact on office energy efficiency
Year 2020 was unprecedented and differed from the previous years in many aspects among which energy use and energy related human behavior are no exception. Specifically, in Spain, during the spring and summer of 2020 all the work was carried out from home due to the lock down. Then, in the autumn, the employees came back to their offices but only partially with limited hours and partially working from their homes. Table 3 shows energy consumption of the office lab in these two periods.
Table 3 Comparison of energy consumption in spring and autumn periods.
|
Spring 2020
|
Autumn 2020
|
|
kWh
|
%
|
kWh
|
%
|
Total energy consumption
|
420.4
|
100
|
322.7
|
100
|
Energy consumption working hours[3]
|
-
|
-
|
72
|
22
|
Energy consumption afterhours[4]
|
-
|
-
|
251
|
78
|
It can be seen that in spite of the lack of physical presence of employees in the spring period, the energy consumption is significantly higher than in the autumn when employees partially returned to the office. At first glance, this could be interpreted as an inverse relationship between occupation and energy consumption. However, it is because some employees used home computers only as terminals, while the actual tasks were performed on the lab computers with remote desktop connection. These computers were not turned off every day after work (as it normally happens), but were turned on 24/7 instead, which explains higher energy consumption. Since during the autumn period, work was carried on in a semi remote mode, the energy consumption was lower. However, still great part of energy (78%) was consumed afterhours. Nevertheless, it is important to stress for both, spring as well as autumn periods that significantly lower energy consumption in comparison with the summer and winter periods was caused by the fact, that energy consumed by the employees at their homes was not taken into account, although it should be included. However, collecting such data in practice is impossible and may be only estimated.
In order to have a deeper insight and show how drastic change of users’ behavior (homeworking) caused by covid-19 outbreak impacted the energy efficiency, a set of indicators has been calculated for the spring and autumn periods and presented in table 4.
Table 4 comparison of energy indicators of the office lab in the spring and autumn periods.
|
m2
|
No. of users
|
kWh
|
m2/ person
|
kWh/ m2
|
kWh/ workstation
|
kWh/ person
|
kWh/ person hours
|
kWh/m2, person hours
|
Spring
|
72
|
0
|
420.4
|
-
|
5.8
|
26.3
|
-
|
-
|
-
|
Autumn
|
4
|
322.7
|
18
|
4.5
|
20
|
80.7
|
4.0
|
56
|
Comparison of values from table 2 and table 4 shows how consideration of users’ behavior is important for more realistic picture of the situation. The number of kWh/m2 for spring and autumn is clearly lower than in the summer and winter periods which may suggest higher efficiency. However, looking at the indicators that include human activity, a more realistic overview can be obtained. kWh/person, kWh/person hours, as well as kWh/m2/person hours are higher in the autumn then summer and winter periods (obtaining these indicators for the spring period was not possible due to the lock down). This is because the more effectively a space is used, the more it consumes in absolute numbers. And the higher the occupancy and space efficiency, the less the building or space tends to appear when the indicator of energy consumption per floor area is used (kWh/m2) since more users render a higher energy demand, while the floor area remains constant (Dooley, 2011; Martani et al., 2012).
3.4. Comfort
User feedback on comfort was investigated to find out possible relation between energy consumption and comfort levels. Comfort can be defined as a condition of mind which expresses satisfaction with the environment (ASHRAE, 1997). Energy consumption may have a direct impact on such a steady state sensation. For this reason, during the summer and winter periods, users were asked to use a mobile crowd sensing app and provide feedback on their level of comfort. Figure 5 shows comparison of general, thermal, visual and acoustic comfort levels in the summer and winter.
The general comfort in the summer and in the winter was close to optimal with 69% and 80% opinions on comfortable and slightly uncomfortable. The result of thermal comfort in the summer period, however is not so clear with 38% of slightly cool and only 20% of hot. Especially surprising is 2% of cold in the summer voted when the indoor temperature during the working hours fluctuated between 26.7°C and 28.7°C while the European standard EN 15251 (2007) defines the summer comfort temperature in the range between 23°C and 26 °C. Figure 6 shows that there is no strict relation between temperature and users’ thermal comfort sensation. Especially it can be seen that the same range of temperatures (25°C - 26°C) in the summer is considered as slightly cool or even cold, while it is considered slightly warm or even hot in the winter.
It can be also observed that lighting conditions were close to optimal (figure 7). The greatest number of votes (81% in the summer and 71% in the winter) described visual comfort as slightly bright. According to the European standard UNI EN 12464-1(2011), a comfortable minimum illumination level should be between 500 and 700 lx and indeed the light intensity in the lab was maintained on the level of >500 lx almost all the time during the experiment. In practice, on many occasions it was much higher especially in the summer time. What is surprising, is that significantly lower light conditions in the winter period have almost no impact on users’ satisfaction which is very comparable for the summer and winter period.
The noise level was rather satisfactory with far majority of votes for silence and low noise and with only few complaints about high noise (figure 8). The maximum registered values did not exceed the level of 80db which is a limit of comfort defined in the standard (EU Directive 2003/10/EC, 2003). Also no significant differences were observed between summer and winter periods. However, it must be stressed that the sensor sensibility was limited and noise levels lower that 50db were not registered.
In terms of gender, the results were almost the same for men and women. However, this result is not objective due to the significant disproportion between the number of representatives of both sexes – 16 males and 2 females in the summer period, and 14 males and 2 females in the winter period.
After all, it is not surprising that even in the optimal conditions that complies with the standard, there will always be some percentage of dissatisfied users. This is because human beings are individuals and there is no unique definition of comfort conditions. Such thesis was already stated fifty years ago saying that there is no ambient condition that can make all individuals feel equally comfortable (Fanger 1970).
3.5 Level of thermal tolerance
Users’ comfort or discomfort sensation is conditioned by a mixture of different environmental factors, such as humidity or atmospheric pressure, which are rather not easy to identify unless they reach extreme values. Nonetheless hot or cold sensation is very easy to pinpoint even if the variation is only of a few degrees. Temperature is the most easily noticeable and most influential factor that determines the level of comfort (Djekic, et al., 2018). For this reason, a level of thermal tolerance of occupants in the office lab was studied. To this end, on 17th of February 2020, without notifying the users, the heating temperature was slowly increased to find out the threshold between thermal comfort and discomfort (sensation of hot). It was expected that at certain point users would start to express their dissatisfaction by choosing the hot option in the crowd sensing app. As it can be seen in figure 9, this moment was reached at about 13:10, when the temperature exceeded the border of 26 °C. After that, the decrease of temperature can be observed until the level of about 24.6 °C, which in this case can be understood as a comfort level. This observation was checked and compared with users’ opinion on comfort to reveal that surprisingly users did not complaint much regarding the temperature. Only one vote at 13:06 indicates hot sensation. The others are more moderate.
Comparing this result with the data form other sensors revealed the cause of the decrease of temperature. At 13:00 all the windows were opened and the heating was turned off. The windows were closed later at 13:50, while the heating remained off for the rest of the day. This demonstrates, that users, in spite of having a possibility to express their opinion on comfort, rather prefer to act instead. They probably cannot see the direct impact of their opinion on the enhancement of comfort. Thus, they prefer to take an action (turn off the heating, open windows) instead of expressing their opinion which does not translates their needs immediately.
3.6. Correlation between energy consumption, comfort level and environmental factors
In order to obtain a deeper insight on energy performance, possible correlations between energy consumption, users’ comfort and environmental factors were investigated. For this purpose, a Pearson correlation coefficient was used to measure the relationship between each of the individual parameters. This indicator calculates the correlation between two variables and returns a value from -1 to 1, where -1 represents a total negative correlation and 1 a total positive correlation; whereas 0 denotes no correlation (Benesty, et al., 2009). First, energy consumption and comfort levels have been tested showing very weak, almost neutral, correlation with the higher result of -0.2 obtained between energy consumption and thermal comfort. This was an expected outcome given that heating and air conditioning weren’t taken into account according to the initial assumptions.
Similarly, no strong correlation between energy consumption and environmental factors has been found. The highest result between energy consumption and humidity equal to 0.4 is too weak to build any binding conclusions on it.
Also, surprisingly, no correlation between comfort levels and environmental factors has been found. Although it was expected to reveal stronger correlation between temperature and thermal comfort, or noise and acoustic comfort which intuitively seem to be an obvious association. The data however reveal no such relation. The highest result of 0.3 was between thermal comfort and noise. This may indicate that the thermal tolerance and the sensibility for other environmental factors of the sample group was too diverse to be able to form a binding conclusions.