Impact of adjustment strategies on building design process in different climates oriented by multiple performance
Introduction
Buildings account for about 40% of global energy consumption and more than 30% of carbon dioxide emissions [1]. The building design process is of wide concern, as most decisions about building sustainability are made at this stage [2]. This is currently highlighted in numerous high-performance building guidelines [3].
Many scholars have researched building design process, including uncertainty assessment of building performance, sensitivity analysis of design parameters, and building performance optimization. Uncertainty assessment refers to analyzing the uncertain distribution of building performance under the influence of various uncertain factors [4]. Sensitivity analysis can be used to identify the important factors affecting building performance [5]. Building performance optimization refers to establishing an optimization model between building design parameters and building performance (such as building energy, indoor thermal comfort), obtaining optimal design solutions using optimization methods [6]. The independent variables involved in these three types of research are steady-state design parameters, such as the building orientation, wall thermal resistance, wall specific heat, window U-value and solar heat gain coefficient (SHGC) [3].
Generally speaking, the main factors affecting building performance can be divided into two types: building design parameters (steady-state) and adjustment strategies (dynamic) [7]. Building design parameters are determined during the building design stage. Adjustment strategies (AS) are closely related to occupant behavior in the building operation stage. Although these adjustment behaviors are unknown at the building design process, research on occupant behaviors shows that occupants generally adjust the shading device and open the window according to self-comfort [8]. Through long-term field surveys in various regions, many studies have revealed some shared behavior patterns. For example, seasonal changes in window-opening behavior: the window-opening frequency was higher in non-heating seasons, especially in the transition season [9]. More important, outdoor and indoor air temperatures directly link to an occupants’ window adjustment decision [10]. Rijal et al. formulated an adaptive algorithm to predict window opening using logistic regression [11]. Some adaptive thermal comfort models can be used to predict occupants’ window opening behavior, based on indoor and outdoor air temperatures [12]. Fiorentini et al. applied adaptive thermal comfort criteria to windows opening in a controller, and its performance was tested via simulations and experiments [13]. For shading, internal blinds are the focus of a majority of studies, because they largely allow occupant responses through manual adjustment [14]. Occupant preference is to avoid visual discomfort with a minimum number of interactions with lighting and blind, and little consideration is paid to the exploitation of daylight dynamically, to offset electric lighting. In general, ensuring the indoor illuminance levels and avoiding glare are prerequisites for optimization analysis of shading control [15].
The impact of AS on the building design process is noteworthy because there is an interaction between AS and design parameters. The upper limit of the effect of shading and window ventilation on building performance is influenced by design parameters. For example, the window ventilation effect is the product of a complex interaction of personal behavior, building design parameters, and the outdoor environment. Its effect is all strongly related to some controllable design parameters, such as window configuration and the window to wall ratio (WWR) [16]. When a larger WWR is set up for greater ventilation, it will lead to extra heat gain through external windows and increase cooling energy demand [17]. Building orientation is also a major factor, the ventilation effect is better when the orientation of the outer window aligns with the dominant wind direction [18]. In turn, the AS also affects the optimal design parameters by changing the original heat transfer structure of building envelopes. For example, shading will change the heat exchange through the external glazing, thereby affecting the choice of SHGC. The demand for SHGC in winter and summer is reversed, which is bound to involve trade-offs. However, when using shading measures in the summer, the external window can adopt glass with a higher SHGC to get more heat gain in the winter [19]. In other words, the robustness of building performance related to operation strategies is often disregarded. A building design scheme that is optimal for one profile of determined design scenarios is not necessarily the optimal solution for most sets of occupants [20].
Only a few studies have discussed the impact of AS on building design process. For example, Chen et al. compared the preferable design solutions of a prototype high-rise residential building under design scenarios of single-sided ventilation and cross-ventilation. Optimization variables include the building layout, envelope thermophysics, building geometry, infiltration, and air-tightness [21]. Based on an office building model in a hot-dry climate, Singh et al. performed uncertainty and sensitivity analyses of energy and visual performance under the influence of external venetian blind shading. Results indicated a large uncertainty in lighting (45%), HVAC (33%) and useful daylight illuminance (106%) [22]. Rouleau et al. quantified the impacts of occupant behavior including opening windows on the residential building performance include energy consumption and comfort. Results show the AS caused great uncertainty in building performance with a coefficient of variation of about 50% [23].
A systematic analysis of the effects of shading and window ventilation on the building design process is seldom addressed by existing research. To bridge this research gap, we identified several AS modes based on the prior probability, then analyzed their impact on the building design process including uncertainty analysis, sensitivity analysis, and building performance optimization. Furthermore, the results of four typical climate regions in China are compared considering climate differences. This research has important reference significance for robust building design.
Section snippets
Literature review
The literature review is mainly focused on methods in building design process, including (1) uncertainty analysis, (2) sensitivity analysis, and (3) building performance optimization.
Methodology
The research framework is composed of four phases: (1) comparison of AS modes, (2) uncertainty analysis, (3) sensitivity analysis, and (4) multi-criteria optimization. The research framework is provided in Fig. 1.
The impact of various strategy modes on building performance.
Fig. 7 shows the influence of various modes of AS on the annual thermal comfort and the annual heating/cooling/lighting energy demands. As shown in Fig. 7(a), shading, night ventilation, and their interactions, all can reduce the CEUI for the four cities. The AS has the greatest potential for cooling energy-saving in Beijing, compared to the other cities. For each city, differences in the cooling energy-saving potential of different mode AS exist. By mode 1 vs mode 3 or mode 2 vs mode 4, night
Conclusion
This paper establishes a framework to analyze the impact of adjustment strategies on the building design process. Based on the principle of occupant's comfort, the adjustment strategy model is established to integrate shading, natural ventilation, and dimming. Considering and not considering adjustment strategies are two design scenarios. Four typical climates in China are compared given their climate differences. The influence of adjustment strategy on building design process is mainly
CRediT authorship contribution statement
Ran Wang: Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Shilei Lu: Funding acquisition, Methodology, Project administration, Supervision. Wei Feng: Validation, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This research has been supported by the “National Key R&D Program of China” (Grant No. 2016YFC0700100).
References (67)
- et al.
Thermal comfort and building energy consumption implications–a review
Appl Energ
(2014) - et al.
Component-based machine learning for performance prediction in building design
Appl Energ
(2018) - et al.
Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China
Appl Energ
(2018) - et al.
A review of uncertainty analysis in building energy assessment
Renew Sustain Energy Rev
(2018) - et al.
Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis
Appl Energ
(2014) - et al.
Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms
Appl Energ
(2020) - et al.
Passive design optimization of low energy buildings in different climates
Energy
(2018) - et al.
A longitudinal study of summertime occupant behaviour and thermal comfort in office buildings in northern China
Build Environ
(2018) - et al.
Occupant behavior regarding the manual control of windows in residential buildings
Energy Build
(2016) - et al.
A study on influential factors of occupant window-opening behavior in an office building in China
Build Environ
(2018)