Developing Indoor Temperature Profiles of Albanian Homes: Comparison
View Latest Version
Please note this is a comparison between Version 2 by JONIDA MURATAJ and Version 3 by Nora Tang.

Oversimplifying occupant behaviour using static and standard schedules has been identified as a limitation of building energy simulation tools. Three statistically different profiles were developed for each summer and winter, indicating that homes are used in different ways, as well as revealing possible comfort requirements. A statistically significant association was found between the presence of children and the clusters in winter, suggesting that families with dependents use more energy. Building-related factors including building type, building age, and wall insulation were found to be statistically significantly associated with clusters in summer. 

  • occupant behaviour
  • indoor temperature profiles
  • cluster analysis
  • energy modelling

1. Influence of Occupant Behaviour in Building Energy Simulations

It is widely recognised that occupant behaviour has a significant impact on building energy demand and on indoor thermal comfort conditions [1][2][3][4][5]. Nonetheless, integrating occupant behaviour into building energy simulations has been found to be challenging [6], as it is usually oversimplified and predefined through static schedules in building performance simulations [2][5][7][8][9][10]. Due to a lack of pre-retrofit evaluation in the early stage of the design and a lack of data about various aspects of the building, considerable assumptions and predictions are used [11][12][13]. Therefore, the improvement of occupants’ presence and modelling their actions in building simulations is essential to enhance the accuracy of the building energy simulation process [14]. Yao and Steemers [15] claimed that the number of occupants and the length of the period in which the dwelling is occupied are the main factors influencing energy consumption. Yan et al.[14] also found that occupant–building interaction is affected by the arrival, departure, and duration of absence of occupants. Jia, Srinivasan, and Raheem [6] described how occupant presence/absence in rooms is not enough for occupant behaviour modelling and how further information is needed regarding the adaptation to the indoor environment and the occupants themselves.
In fact, the indoor air temperature has been found to significantly influence the performance of energy-efficiency measures [16] and assuming one standard indoor temperature profile for energy modelling for all homes would contribute to the performance gap between the actual and estimate energy consumption in homes. Developing various indoor temperature profiles would overcome this assumption and would assist in better calculations of energy consumption in homes as well as closer predictions of energy savings through energy retrofitting.
However, the indoor temperature is highly affected by occupant behaviour, which itself can be affected by various factors, including a building’s characteristics, social and personal characteristics, rebound effects, personal comfort, lifestyle and cultural background, occupants’ knowledge and skills, and occupants’ experience [17]. According to [18][19], there are also social-psychological factors that may influence thermal comfort and energy behaviours, including beliefs, values, norms, social trust, habits, energy saving attitudes, motivations, perceived behavioural control, and environmental concerns. Furthermore, Ortiz, Itard, and Bluyssen [20] support the claim that occupant behaviours are interplays of personal, environmental, and social factors and that their actions are influenced by the way the occupants understand energy, control, and comfort. Ebrahimigharehbaghi et al. [21] consider the multiple origins of the factors that influence occupant behaviour and categorise them as motivations, barriers, and contextual and personal factors. Fabi et al. [22] classified these factors into five groups: physical environmental factors, contextual factors, psychological factors, physiological factors, and social factors. Tam, Almeida, and Le  [23] grouped the factors influencing occupant behaviour into objective factors, including environmental conditions such as temperature, air velocity, climate, and noise, and subjective factors, which depend on the personal perception of comfort and are affected by age, metabolic activity, a particular mood, habits, sensations, and social interaction. According to Schaffrin and Reibling [24], all forms of consumption energy practices demonstrate lifestyle choices and can be a form of self-expression.
In this context, to investigate the effect of occupant behaviour on the indoor temperature in homes, this study investigates the association of indoor temperature profiles with socio-demographic characteristics (education level, household size, presence of children or retired persons, income, and monthly energy bill), as well as behavioural factors (cooling and heating usage patterns during the day and night) for each household selected, along with the effect of building characteristics (building type and size, period of construction, existence of wall insulation or double glazing, and type of heating or cooling).

2. Factors Affecting Indoor Temperatures in Dwellings

The indoor temperature has been found to significantly influence the performance of energy retrofits [16]. The setpoint temperature for heating and the duration of the heating period is the greatest influential factors on energy consumption for space heating in homes [25][26][27][28][29][30]. An increase in energy consumption of 10% has been calculated for each degree of indoor temperature in a study undertaken by Tommerup, Rose, and Svendsen [31] on single-family houses in Denmark. Peng et al. [32] found significant variations in building performance using three classifications of occupant behaviours based on an air-conditioning operation schedule, indoor temperature preference, and household loads based on the division type. Bruce-Konuah, Jones, and Fuertes [33] found that physical environmental variables, including the indoor and outdoor temperature, indoor relative humidity, and solar radiation, affect occupants’ behaviour in terms of manual space heating override during the heating season. They found an average increase in space heating energy consumption of 21.5% and 13.6% on weekdays and weekends, respectively. There are also other factors that may lead to indoor temperature profiles differing from heating demand profiles [34], and having distinct temperature profiles would indicate not only that homes are used in different ways by different occupants, but could also indicate various comfort requirements. Kelly et al.  [35] developed a model to predict the daily mean internal temperatures for a national building stock and found behavioural and socio-demographic properties of the occupants’ significant variables among the physical properties of the building, the external climate, and the dwelling’s geographic location to explain internal temperature demand. Hunt and Gidman [36], using a national field survey of house temperatures in the UK, found that a household’s income was a strong indicator of room temperature, with lower-income homes found to be 3 °C lower on average in the heating season.
Notwithstanding the many uncertainties, default assumptions about how homes are heated and cooled, and at what temperatures, have been used for modelling energy consumption of housing stock [37]. Heating temperatures of 21 °C have been assumed for modelling housing stock and estimating energy savings from retrofitting strategies in the UK [38]. Al-Mumin, Khattab, and Sridhar [39] found a variation in thermostat setpoints from below 19 °C to above 25 °C after investigating summer air-conditioning use in residential buildings. Moreover, Peng et al. [32] found that air-conditioning operations within a household varied every day. Default setpoint temperatures of 20 °C and 26 °C for heating and cooling, respectively, have been used in a study of energy retrofitting of residential stock in Albania [40]. This assumption leads to energy consumption and energy saving estimations largely different from the actual ones [34].
Notwithstanding that the heating setpoint effect has been largely acknowledged as one of the main factors that affect the interaction between the occupants and the building, there is still no standard method to assign the heating setpoint for building simulation [41]. Therefore, developing temperature profiles would decrease the estimations coming from the use of a single heating and cooling setpoint, as well as the default heating and cooling duration in homes.

References

  1. Sunikka‐Blank, M.; Galvin, R. Introducing the prebound effect: The gap between performance and actual energy consumption.Build. Res. Inf. 2012, 40, 260–273
  2. Ben, H.; Steemers, K. Modelling energy retrofit using household archetypes. Energy Build. 2020, 224, 110224
  3. Gram‐Hanssen, K.; Georg, S. Energy performance gaps: Promises, people, practices. Build. Res. Inf. 2017, 46, 1–9.
  4. Steemers, K.; Geun Young, Y. Household energy consumption: A study of the role of occupants. Build. Res. Inf. 2009, 37, 625–637
  5. Ascione, F.; Bianco, N.; De Masi, R.F.; Mastellone, M.; Mauro, G.M.; Vanoli, G.P. The role of the occupant behavior in affectingthe feasibility of energy refurbishment of residential buildings: Typical effective retrofits compromised by typical wrong habits.Energy Build. 2020, 223, 110217
  6. Jia, M.; Srinivasan, R.; Raheem, A. From occupancy to occupant behavior: An analytical survey of data acquisition technologies,modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renew. Sustain. Energy Rev. 2017,68, 525–540
  7. Hong, T.; Yan, D.; D’Oca, S.; Chen, C.‐F. Ten questions concerning occupant behavior in buildings: The big picture. Build. Environ.2017, 114, 518–530
  8. Gaetani, I.; Hoes, P.‐J.; Hensen, J. Occupant behavior in building energy simulation: Towards a fit‐for‐purpose modeling strategy.Energy Build. 2016, 121, 188–204
  9. Menezes, A.C.; Cripps, A.; Bouchlaghem, D.; Buswell, R. Predicted vs. actual energy performance of non‐domestic buildings:Using post‐occupancy evaluation data to reduce the performance gap. Appl. Energy 2012, 97, 355–364
  10. Laaroussi, Y.; Bahrar, M.; El Mankibi, M.; Draoui, A.; Si‐Larbi, A. Occupant presence and behavior: A major issue for buildingenergy performance simulation and assessment. Sustain. Cities Soc. 2020, 63, 102420
  11. Demanuele, C.; Tweddell, T.; Davies, M. Bridging the gap between predicted and actual energy performance in schools. InProceedings of the XI World Renewable Energy Congress, Abu Dhabi, United Arab Emirates, 25–30 September 2010
  12. Zero Carbon Hub. Closing the gap between design and as‐built performance. Evidence review report; Zero Carbon Hub: London, UK,2014
  13. Bordass, B.; Leaman, A.; Ruyssevelt, P. Assessing building performance in use 5: Conclusions and implications. Build. Res. Inf.2001, 29, 144–157
  14. Yan, D.; O’Brien, W.; Hong, T.; Feng, X.; Gunay, H.B.; Tahmasebi, F.; Mahdavi, A. Occupant behavior modeling for buildingperformance simulation: Current state and future challenges. Energy Build. 2015, 107, 264–278
  15. Yao, R.; Steemers, K. A method of formulating energy load profile for domestic buildings in the UK. Energy Build. 2005, 37, 663–671
  16. Dodoo, A.; Tettey, U.Y.A.; Gustavsson, L. On input parameters, methods and assumptions for energy balance and retrofit analysesfor residential buildings. Energy Build. 2017, 137, 76–89
  17. Zou, P.X.; Xu, X.; Sanjayan, J.; Wang, J. Review of 10 years research on building energy performance gap: Life‐cycle and stakeholderperspectives. Energy Build. 2018, 178, 165–181
  18. Chen, C.‐F.; Xu, X.; Frey, S. Who wants solar water heaters and alternative fuel vehicles? Assessing social–psychological predictorsof adoption intention and policy support in China. Energy Res. Soc. Sci. 2016, 15, 1–11
  19. Abrahamse, W.; Steg, L.; Vlek, C.; Rothengatter, T. A review of intervention studies aimed at household energy conservation.J. Environ. Psychol. 2005, 25, 273–291
  20. Ortiz, M.; Itard, L.; Bluyssen, P.M. Indoor environmental quality related risk factors with energy‐efficient retrofitting of housing:A literature review. Energy Build. 2020, 221, 110102
  21. Ebrahimigharehbaghi, S.; Qian, Q.K.; de Vries, G.; Visscher, H.J. Identification of the behavioural factors in the decision‐makingprocesses of the energy efficiency renovations: Dutch homeowners. Build. Res. Inf. 2021, 50, 369‐393
  22. Fabi, V.; Andersen, R.V.; Corgnati, S.P.; Olesen, B.W. A methodology for modelling energy‐related human behaviour: Applicationto window opening behaviour in residential buildings. Build. Simul. 2013, 6, 415–427
  23. Tam, V.W.Y.; Almeida, L.; Le, K. Energy‐Related Occupant Behaviour and Its Implications in Energy Use: A ChronologicalReview. Sustainability 2018, 10, 2635
  24. Schaffrin, A.; Reibling, N. Household energy and climate mitigation policies: Investigating energy practices in the housingsector. Energy Policy 2015, 77, 1–10
  25. Firth, S.K.; Lomas, K.J.; Wright, A.J. Targeting household energy‐efficiency measures using sensitivity analysis. Build. Res. Inf.2010, 38, 25–41
  26. Hughes, M.; Palmer, J.; Cheng, V.; Shipworth, D. Sensitivity and uncertainty analysis of England’s housing energy model. Build.Res. Inf. 2013, 41, 156–167
  27. Kane, T.; Firth, S.; Lomas, K. How are UK homes heated? A city‐wide, socio‐technical survey and implications for energy modelling.Energy Build. 2015, 86, 817–832
  28. Magalhães, S.M.; Leal, V.; Horta, I. Modelling the relationship between heating energy use and indoor temperatures in residentialbuildings through Artificial Neural Networks considering occupant behavior. Energy Build. 2017, 151, 332–343
  29. Jones, R.V.; Fuertes, A.; Boomsma, C.; Pahl, S. Space heating preferences in UK social housing: A socio‐technical householdsurvey combined with building audits. Energy Build. 2016, 127, 382–398
  30. Cheng, V.; Steemers, K. Modelling domestic energy consumption at district scale: A tool to support national and local energypolicies. Environ. Model. Softw. 2011, 26, 1186–1198
  31. Tommerup, H.; Rose, J.; Svendsen, S. Energy‐efficient houses built according to the energy performance requirements introducedin Denmark in 2006. Energy Build. 2007, 39, 1123–1130
  32. Peng, C.; Yan, D.; Wu, R.; Wang, C.; Zhou, X.; Jiang, Y. Quantitative description and simulation of human behavior in residentialbuildings. Build. Simul. 2011, 5, 85–94
  33. Bruce‐Konuah, A.; Jones, R.V.; Fuertes, A. Physical environmental and contextual drivers of occupants’ manual space heatingoverride behaviour in UK residential buildings. Energy Build. 2018, 183, 129–138
  34. Huebner, G.M.; McMichael, M.; Shipworth, D.; Shipworth, M.; Durand‐Daubin, M.; Summerfield, A.J. The shape of warmth:Temperature profiles in living rooms. Build. Res. Inf. 2015, 43, 185–196
  35. Kelly, S.; Shipworth, M.; Shipworth, D.; Gentry, M.; Wright, A.; Pollitt, M.; Crawford‐Brown, D.; Lomas, K. Predicting the diversityof internal temperatures from the English residential sector using panel methods. Appl. Energy 2013, 102, 601–621
  36. Hunt, D.; Gidman, M. A national field survey of house temperatures. Build. Environ. 1982, 17, 107–124
  37. Kavgic, M.; Mavrogianni, A.; Mumovic, D.; Summerfield, A.; Stevanovic, Z.; Djurovic‐Petrovic, M. A review of bottom‐upbuilding stock models for energy consumption in the residential sector. Build. Environ. 2010, 45, 1683–1697
  38. Anderson, B.R BREDEM‐8 Model Description: 2001 Update, BR439; IHS BRE Press: Watford, UK, 2002
  39. Al‐Mumin, A.; Khattab, O.; Sridhar, G. Occupants’ behavior and activity patterns influencing the energy consumption in theKuwaiti residences. Energy Build. 2003, 35, 549–559
  40. Novikova, A.; Szalay, Z.; Simaku, G.; Thimjo, T.; Salamon, B.; Plaku, T.; Csoknyayi, T. The typology of the residential buildingstock in Albania and the modelling of its low‐carbon transformation. In Support for Low‐Emission Development in South EasternEurope (SLED); Regional Environmental Center: Szentendre, Hungary, 2015
  41. Guerra‐Santin, O.; Silvester, S. Development of Dutch occupancy and heating profiles for building simulation. Build. Res. Inf.2016, 45, 396–413
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback