Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy

Abstract

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On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions

“…While this is certainly true, in reality, modelling complexity is continuous rather than discrete, as a category (e.g. stochastic models) can be characterized by different complexities according to the model's size and resolution (Gaetani, Hoes, and Hensen 2016).…”

“…While this concept was introduced earlier (Gaetani, Hoes, and Hensen 2016), the value of the present study is that it offers a practical method to separate influential and non-influential aspects, and it demonstrates the validity of the hypothesis.…”

“…Hence, the choice of the most suitable modelling complexity should be dependent on the considered case and purpose of the simulation (Gaetani, Hoes, and Hensen 2016;Mahdavi and Tahmasebi 2016).…”

Identifying Energy Savings Opportunities for a Multi-use Venue Building

“…For example, the local control of systems such as lighting, window and door openings, and small power loads, have been shown to have a significant impact on actual energy consumption through numerous studies (Rijal et al 2009;Menezes et al 2014;Borgeson & Brager 2008). Additionally, occupant related impacts on internal heat gains and extended hours of building use have been shown to impact energy consumption (Brown et al 2010;Gaetani et al 2016). However, there remains a lack of research seeking to bridge the divergence between these two research traditions.…”

Household archetypes and behavioural patterns in UK domestic energy use

“…Collecting and employing an exhaustive dataset on occupant behaviour for each household in home energy audits is likely to be unrealistic. Current behavioural modelling using mainly stochastic methods in building simulations addresses behavioural complexity but excludes occupants' sociodemographics and household characteristics which are crucial for identifying specific target groups (Andersen et al 2016;Gaetani et al 2016). An alternative is to create an archetype for each significant class of household based on statistical analysis, and then examine different ways of tackling energy efficiency according to the characteristics for that archetype.…”