In order to gain a better understanding on time use in the local context, the survey framework presented in Chapter 4 (see Annex A) was distributed to and answered by 453 respondents of households located in Northern Italy (PAPER XIV). For shaping energy-related activities, the respondents were asked to report their activities performed at home during the last full day, choosing for every 15- minutes intervals among the proposed activities. This means that, while the responses of the Danish TUS had to be clustered into energy-related activities, here the latter could be directly investigated. This analysis therefore allows also for validating to some extent the effectiveness of the clustering procedure. However, unlike the Danish TUS data, it is necessary to highlight that the Italian sample cannot be considered representative for the wider Italian population, since the survey was compiled (online) mainly by respondents aged between 20 and 30 (60% of respondents). Indeed, further investigation is needed in order to extend the study to a more inclusive sample.
Similar to the results obtained based on the Danish TUS, in relation to weekdays activity profile (Figure 5.4-3a), sleeping was the dominant activity - 90% of the survey respondents were asleep between 2:30 and 6:00. During the weekdays
the respondents have spent a significant portion of their time outside the home. Most of the respondents were not present at the home from 9:00 am to 18:30, however a part of respondents were back home during the lunch time (between 13:00 and 14:00). There are two evident peaks for „eating” activity during the lunch (around 13:30) and dinner time (around 20:30). It is possible to distinguish the peak for „TV/IT entertainment” activity in the evening hours- from 21:00 pm to 23:00 pm. The percentage of respondents who were doing „House cleaning/washing clothes” and „Practical work” activities were quite constant during all day.
(a)
(b)
Figure 5.4-3. Daily activity profiles in investigated Italian households during (a) weekdays and (b) weekends (b).
Not at home
Others
Not at home
In relation to weekend activity profile (Figure 5.4-3b), it is possible to notice that Danish and Italian respondents have spent much more time at home comparing to weekdays. The highest percentage of Italian respondents who were out of home (around 40 % of respondents) was at 11:30 and in the afternoon hours, from 15:30 to 19:00. The „sleeping” activity duration was slightly longer than during the weekdays - more than 90 % of respondents slept from 2:00 to 7:00. During the weekend the occupants spent much more time on „Free/family time” activity- with the highest peak in the afternoon, from 15:30 to 17:30. Similar to weekdays, there are two significant peaks for „Eating” activity- from 12:30 to 14:30 and from 19:30 to 21:30. During the weekend, the respondents spent slightly more time on „Cooking/washing dished activities” than during the weekends- there are two peaks in the lunch/dinner time- from 12:30 to 13:30 and from 19:30 to 20:30. For the „TV/ IT entertainment” activity the respondents had similar preferences as during the weekdays- more than 30 % of occupants have spent their time watching TV or navigating internet from 21:30 to 23:00 pm. „House cleaning/washing clothes” and „Practical work” activities were distributed during the day and there are no significant peaks which could indicate the preferable time for those activities.
Figure 5.4-4. Comparison (Italian households vs Danish TUS) of daily time spent on the activities during summer.
Figure 5.4-4 represents the average time which the respondents have spent on the 10 energy-related activities in 24 hours during the weekdays and weekend compared to the Danish TUS-based investigation. The key results of these analysis were:
longer sleeping times during weekends (8h 16m) with respect to weekdays (7h 17m) – Danish respondents reported to sleep longer with respects to the Italian respondents;
longer occupant presence within the residence during weekends (18h 02m) with respect to weekdays (16h 25m) – Italian respondents reported to be out of home longer than Danish respondents;
Italian respondents have longer free/family time during the weekends (1h 55m) than during the weekdays (1h 15m) and with respect to the Danish respondents; Danish respondents reported longer time spent for TV/IT entertainment activity
during the weekends and weekends with respect to the Italian respondents; Italian respondents spent slightly longer time on cooking/washing dishes, while
Danish respondents report slightly longer eating activities;
no significant difference for time spent for personal care activity during the weekends and weekdays.
Finally, Figure 5.4-5 shows occupancy profiles based on the reported activities from the Danish and Italian respondents. This comparison highlights that Italian respondents in average reported to come home later with respect to the Danish respondents. The trend of higher percentages of respondents at home during weekends can be observed in both samples.
Figure 5.4-5. Occupancy profiles based on the Danish TUS and the Italian time use survey in North Italian households for a typical weekday and weekend day.
5.5 Perspectives and challenges
The analysis provided in this study demonstrated that TUS data provides valuable information for developing enhanced building simulation inputs for modelling occupant behaviour and its influence on energy consumption in the residential sector. Based on the Danish TUS data, daily profiles of ten energy- and occupancy-related activities were different depending on the season day of the week (weekdays and weekends). Survival curves of the daily time durations of the activities provided typical starting/ending times of each activity and representative occupancy profiles for different household typologies during weekdays and weekends. Furthermore, during weekdays occupants were most likely to leave their home at 08:00 or 13:00 and tended to return at noon or in the late afternoon/early evening hours (18:00). To enhance building simulation inputs for occupancy in the Danish residential sector, online access to a spectrum of individual occupancy profiles for different household typologies and different days of the week is provided.
The outcomes were in line with typical trends of hourly electricity profiles in Danish households. Indeed, similar peak values of hourly electric load profiles and some energy-related activities were observed during the same hours of the day. In detail, these peaks referred to the early morning hours, lunch time and dinner time, and could therefore be strictly correlated to cooking and eating activities. The Danish TUS data provided occupancy patterns similar to an existing simplified occupancy profile developed by the U.S DOE. The Danish TUS is an important source for developing more accurate energy-related occupant behaviour profiles in Danish households. However, future work is necessary to further explore the TUS data and extracted occupancy patterns to create stochastic models that can be implemented in dynamic energy simulation programs towards bridging the gap between predicted and real energy consumptions. Further work should also investigate the link between energy-related activities and respective electricity demands in order to define high resolution demand profiles in Danish and Italian households.
5.6 Publications
PAPER XI Barthelmes, V.M., Li, R., Andersen, R.K., Bahnfleth, W., Corgnati, S.P., Rode, C. (2018). Profiling Occupant Behaviour in Danish Dwellings using Time Use Survey Data – Part I: Data Description and Activity Profiling. In: Proceedings of the 4th International Conference on Building Energy & Environment 2018, 5-9 February, Melbourne, Australia, pp. 97-102.
PAPER XII Barthelmes, V.M., Li, R., Andersen, R.K., Bahnfleth, W., Corgnati, S.P., Rode, C. (2018). Profiling Occupant Behaviour in Danish Dwellings using Time Use Survey Data – Part II:
Time-related Factors and Occupancy. In: Proceedings of the 4th International Conference on Building Energy & Environment 2018, 5-9 February, Melbourne, Australia, pp. 103-108.
PAPER XIII Barthelmes, V.M., Li, R., Andersen, R.K., Bahnfleth, W., Corgnati, S.P., Rode, C. (2018). Profiling occupant behaviour in Danish dwellings using time use survey data, Energy and Buildings 177, pp. 329-340.
PAPER XIV Barthelmes, V.M., Becchio, C., Crespi, G., De Nicoli, M., Fabi, V., Corgnati, S.P. (2019). Profiling Occupant Behaviour in Italian Households for enhanced building simulation input: Insights Into A Survey-based Investigation. Paper accepted at the Building Simulation conference, 2-4 September, Rome, Italy.
Chapter 6
Development of Energy
Engagement campaigns for
behavioural change
6.1 Overview
The previous chapters were aimed at modelling and profiling occupant behaviour with focus on individuals (Chapter 4) and large-scale investigations (Chapter 5). Chapter 3 and 4 explored key drivers of the human-building interaction based on the assumption that different occupant behaviour lifestyles can significantly impact building energy use and thermal comfort conditions of the occupants. Indeed, Chapter 1 highlighted that energy efficiency goals can be only met if the occupants adopt an aware attitude concerning their energy-related actions and consequences on energy use. Indeed, raising awareness among building occupants on how their behaviour, comfort criteria settings, and lifestyles affect building energy use has become a central topic of innovative energy efficiency strategies. Reaching European energy efficiency goals does not only require the optimization of building design and features, but also necessitates the real energy consumers to be more aware of their energy-related inter-actions with the building. However, motivating occupants to change their behaviour can become a challenging task. It is essential to provide novel, stimulating, and easily understandable information that help triggering a more energy-friendly behaviour on a daily basis. Motivations of the occupants to change their behaviour can differ significantly from context to context (residences, offices, public buildings). In line with this, the chapter is aimed at presenting methodological frameworks of energy engagement campaign to which I contributed during my Ph.D studies. Although if
these campaigns investigate different typologies of feedback and triggers and/or communication tools, it is possible to identify a general framework that is implemented in all the campaigns. Figure 6.1-1 shows the overview of Chapter 5 depicting the human in the loop and a schematic process that reflect the overall strategy of behavioural change investigations. In this Chapter, three different energy engagements are analysed and compared following the structure of the elements shown in the loop (sensor network and data collection, data analytics and feedback, and communication tools). This chapter is aimed at contributing to the following research questions:
How to leverage efficiently behavioural change through innovative triggers (e.g. health, comfort, peer comparison)?
How to assess and evaluate behavioural change and related campaigns? What analytical solutions can be developed for feedback provision? How much energy can be saved through behavioural change?