An activity-based modelling framework for quantifying occupants’ energy consumption in residential buildings

Abstract

The residential building is a major energy consumer and pollution source worldwide. The shift towards constructing energy-efficient buildings is impelling higher performance. In sustainable building, occupants become a major source of uncertainty in energy consumption. Yet, energy simulation tools often account for occupant behaviour through predefined fixed consumption profiles. Therefore, energy and buildings experts are in need for more precise methods for better forecasting the influence of occupants on the building performance. An activity-based framework for quantifying occupant-related energy consumption is proposed. The energy consumption is quantified per domestic activity as a function of households' socio-demographic and economic attributes. The aggregation of such domestic activity energy consumption provides an accurate estimation of the household energy consumption per daily, monthly and annually periods. First, a literature review about residential energy consumption and the existing modelling approaches is presented. Second, a systematic breakdown structure of energy end-uses is proposed. The activity-based framework is then introduced. An application example is demonstrated together with simulation results. Finally, model's utility is outlined and its possible implications are discussed.The residential sector consumes secondary energy, i.e. electricity and hydrogen produced from primary energy sources such as coal, natural gas, petroleum, nuclear energy and renewable energy sources, which is used by occupants for performing their domestic activities. Several studies pointed out the major end-use groups of secondary energy such as space heating, space cooling, domestic hot water, as well as appliances and lighting [14][15][16][17]. Building's energy consumption is highly dependent on the performance of its systems and the general behaviour of the occupants [18]. According to Robinson [19], the most complex processes taking place within buildings are those that result from human behaviour. Authors such as Emery and Kippenhan [20], Masoso and Grobler (2010), and Guerra-Santin and Itard [21] also reveal occupants' influence on residential energy consumption. Authors such as Page et al. [22], Robinson [19] and Wilke et al. [18] show that the influence of occupants can be modelled by their presence, the actions they perform (activities such as cooking, using light, etc.), as well as their interactions with the controls of inherent building systems designed for adjusting the indoor environment (e.g., lighting and HVAC).

Past and Future Trends on the Effects of Occupant Behaviour on Building Energy Consumption

“…Similar to qualitative papers, more than half of the quantitative studies focused on energy consumption (65%). Among all quantitative reviewed papers, the most popular subject that has drawn the researcher's attention is that to what extent and how occupant behaviour can affect energy consumption (Becchio et al, 2016;Burgas, Melendez, & Colomer, 2014;Deng & Chen, 2019;Engvall, Lampa, Levin, Wickman, & Öfverholm, 2014;Leroy & Yannou, 2018;Norford et al, 1994;Park & Kim, 2012;Peng, Rysanek, Nagy, & Schlüter, 2018;Rafsanjani & Ahn, 2016;Rafsanjani, Ahn, & Chen, 2018;Santin, 2011;Song et al, 2017;Virote & Neves-Silva, 2012; Z. Wang, Zhao, Lin, Zhu, & Ouyang, 2015; S. Wei, Jones, & de Wilde, 2014; Y. Wei et al, 2019;Xie, Pan, Jia, Xu, & Huang, 2019;Zhun Yu, Fung, Haghighat, Yoshino, & Morofsky, 2011;Yu Zhun Jerry, Haghighat, Fung, Morofsky, & Yoshino, 2011;Yun et al, 2014;Yun & Kim, 2012).…”

“…In the second decade, more researches with diverse approaches, are observed investigating the side effects of this discrepancy and a further investigation of the reasons. For instance, some researches have been conducted with an economic approach dealing with the financial break of sustainable building projects due to inaccuracy of energy estimation and payback period (Sorrell et al, 2009) as well as other researches investigating difficulties in modelling behaviour due to psychological reasons (Indraganti & Rao, 2010;Juodis et al, 2009;Leroy & Yannou, 2018).…”

“…Occupant interaction with the building can vary a lot including opening windows and blinds, use of HVAC systems, changes in setpoints due to thermal preference, maintenance and equipment efficiency, use of equipment and lightings, using hot water, etc (Delzendeh et al, 2017;Gilani, Brien, Gilani, & Brien, 2017). Occupants and their thermal behaviour are taken into account in simulation studies but recent studies have revealed that this factor has a great direct influence on total consumption and is more complex and dynamic than was assumed till now (Becchio et al, 2016;Kim et al, 2017;Leroy & Yannou, 2018).…”

Developing occupant archetypes within urban low-income housing: A case study in Mumbai, India

“…Several studies on residential occupant behaviour (OB) exist in the current literature ranging from modelling occupant diversity and stochasticity (Causone, Carlucci, Ferrando, Marchenko, & Erba, 2019;Diao, Sun, Chen, & Chen, 2017;Haldi, Calì, Andersen, Wesseling, & Müller, 2017;Jang & Kang, 2016;Jones, Fuertes, Gregori, & Giretti, 2017), quantifying the impact of OB (Kim, De Dear, Parkinson, & Candido, 2017;Leroy & Yannou, 2018;Malik, Bardhan, & Banerji, 2019), to identifying key influencing factors (Bedir, Hasselaar, & Itard, 2013;Du, Yu, & Pan, 2020;Esmaeilimoakher, Urmee, Pryor, & Baverstock, 2016;Huang, 2015;Kavousian, Rajagopal, & Fischer, 2013;Rinaldi, Schweiker, & Iannone, 2018). The findings from the extant literature indicates that occupant behaviour is influenced by socio-demographic characteristics; behavioural actions and lifestyle practices (appliance usage, curtailment behaviour, energy habits); and psychographic characteristics (energy attitude, preferences, beliefs or motivation) (Ek & Söderholm Patrik, 2010;Langevin, Gurian, & Wen, 2013a;Ortiz & Bluyssen, 2018;Sanquist, Orr, Shui, & Bittner, 2012;Vogiatzi et al, 2018;Young & Steemers, 2011).…”

How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom

“…The relationship between activities and energy demand has recently become an active field of research [7][8][9]. The recent efforts to produce high-resolution end-use energy demand profiles using bottom-up approaches has highlighted the role of activities in overall demand profiles [10,11].…”