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This paper presents a critical review of studies addressing the effects of window views, daylighting, and lighting on occupant behavior, perception, performance, and well-being. A systematic search in the Scopus database was performed in November 2021 and yielded 515 hits. Seventy-six studies were selected according to the following criteria: i) papers presenting research results with participants' responses and ii) accounting for daylighting, lighting, or window view assessments. The study and setting features, participants, predictors and outcomes, the statistical approach, the reported significance level, and study limitations were extracted from each paper. The analysis then identified differences in the effect of the predictor on the outcome. The number of records obtained through the structured analysis was 240. A wide range of predictors have been used in indoor daylighting, lighting, and window view research to assess people's responses. The most commonly used predictors were artificial lighting features and window features -qualitative predictors - followed by some properties of the light sources, such as correlated color temperature, spectral distribution and horizontal illuminance. Differences in the methods for data collection, the used predictors, the outcomes, and the ways to measure the outcomes generated inconclusive results or results that were limited to the specific study. We extracted data from selected studies to suggest a simple model for the prediction of occupant performance from illuminance and correlated color temperature. The data points were few and scattered and the model, therefore, suffers from considerable uncertainties. Yet, it quantifies performance effects in ranges that are comparable with similar models from other indoor environment domains. [Display omitted] • Most used predictors were artificial lighting features and window features. • Frequently used quantitative predictors were CCT and illuminance. • Differences in the methods, predictors and outcomes generate inconclusive results. • An initial model to quantify occupant performance based in E and CCT was suggested. [ABSTRACT FROM AUTHOR]

Building occupants are continuously exposed to multiple indoor environmental stimuli, including thermal, visual, acoustic, and air quality related factors. Moreover, personal and contextual aspects can be regarded as additional domains influencing occupants' perception and behaviour. The scientific literature in this area typically deals with these multiple stimuli in isolation. In contrast to single-domain research, multi-domain research analyses at least two different domains, for example, visual and thermal. The relatively few literature reviews that have considered multi-domain approaches to indoor-environmental perception and behaviour covered only a few dozen articles each. The present contribution addresses this paucity by reviewing 219 scientific papers on interactions and cross-domain effects that influence occupants' indoor environmental perception and behaviour. The objective of the present review is to highlight motivational backgrounds, key methodologies, and major findings of multi-domain investigations of human perception and behaviour in indoor environments. The in-depth review of these papers provides not only an overview of the state of the art, but also contributes to the identification of existing knowledge gaps in this area and the corresponding need for future research. In particular, many studies use "convenience" variables and samples, there is often a lack of theoretical foundation to studies, and there is little research linking perception to action. • Building occupants are continuously exposed to multiple indoor environmental stimuli. • Personal and contextual aspects are additional domains influencing occupants' perception and behaviour. • Scientific literature in this area typically deals with these multiple stimuli in isolation. • The present contribution reviews 219 multi-domain papers on occupants' indoor environmental perception and behaviour. • Many studies use convenience variables and samples, lack theoretical foundation, and too few link perception to actions. [ABSTRACT FROM AUTHOR]

When simulating the hygrothermal behaviour of a building component, there are many inherently uncertain parameters. A probabilistic evaluation takes these uncertainties into account, allowing a more dependable assessment of the hygrothermal behaviour. However, this often necessitates many Monte Carlo simulations, which easily become computationally inhibitive. To overcome this time-expense problem, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which aims at mimicking the original model with a strongly reduced calculation time. In this paper, a metamodel is developed to directly predict hygrothermal time series (e.g. temperature, relative humidity, moisture content), rather than single-valued derived performance indicators (e.g. maximum mould index), as these hygrothermal time series yield more information, and also allow the user to post-process the output as desired. So far, no metamodelling strategies able to tackle time series are available in the field of building physics. Because the hygrothermal response of a building component is highly non-linear and transient, this paper focuses on neural networks for time series, as they have proven successful in many other fields. The performance and training time of three popular types of networks (multilayer perceptron, recurrent neural network, convolutional neural network) is evaluated based on an application example of a massive masonry wall. The results indicate that only the recurrent and convolutional networks are able to capture the complex patterns of the hygrothermal response. Additionally, the convolutional network performed significantly better and was 10 times faster to train for the current application example, compared to the recurrent network. • A metamodel with a memory mechanism is required to accurately predict hygrothermal time series. • Recurrent neural networks and dilated causal convolutional networks are able to capture the complex patterns of the hygrothermal response. • To predict the relative humidity, dilated causal convolutional neural networks perform significantly better than recurrent neural networks. • Dilated causal convolutional networks are 10 times faster to train on the current example, compared to recurrent neural networks (LSTM, GRU). [ABSTRACT FROM AUTHOR]

Individual behavior of occupants, which is quite complex, greatly affects the route choice, the evacuation time and the efficiency. The phenomenon of the kin behavior often occurs during evacuation. In this paper, a two-dimensional Cellular Automata model is applied to simulate the process of evacuation with respect to the kin behavior. Many interesting phenomena during real evacuation, such as incoherence, jamming, gathering, backtracking and waiting, are simulated by the variation of the building structure, the occupants'' arrangement, the kin attraction and the route selection. We conclude that the psychology of going with the crowd is not always harmful; the conclusions about the kin behavior in one-exit case are the same as those in two-exits case; the number of the sub-groups and the members in each sub-group influence the evacuation efficiency a lot; the typical phenomena of waiting and backtracking in real evacuation do reduce the evacuation efficiency. We have preliminarily found that the proper kin behavior is beneficial to improve the evacuation efficiency. [Copyright &y& Elsevier]

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale. • We define occupant-centric urban data and the pipeline to process it. • We present the different sources of urban data that are useful for data-driven modelling. • We categorise the range of applications for urban behaviour and energy modelling. • We present the recent data-driven modelling approaches for occupant behaviour and energy modelling. • We present a set of recommendations for modelling of occupant behaviour and energy in buildings at urban scale. [ABSTRACT FROM AUTHOR]