Elsevier

Applied Energy

Volume 266, 15 May 2020, 114822
Applied Energy

Impact of adjustment strategies on building design process in different climates oriented by multiple performance

https://doi.org/10.1016/j.apenergy.2020.114822Get rights and content

Highlights

  • The effect of the adjustment strategy on the building design process is analyzed.

  • Uncertainty analysis, sensitivity analysis, performance optimization are involved.

  • Shading, window ventilation, and dimming are considered in the adjustment strategy.

  • Energy, thermal and visual comfort are included in building performance.

  • Various climate zones in China are compared given the impact of climate.

Abstract

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China’s typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios.

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)

  • H.B. Rijal et al.

    Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings

    Energy Build

    (2007)
  • J. Langevin et al.

    Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors

    Build Environ

    (2015)
  • M. Fiorentini et al.

    Development and evaluation of a comfort-oriented control strategy for thermal management of mixed-mode ventilated buildings

    Energy Build

    (2019)
  • K. Van Den Wymelenberg

    Patterns of occupant interaction with window blinds: a literature review

    Energy Build

    (2012)
  • H.B. Gunay et al.

    Development and implementation of an adaptive lighting and blinds control algorithm

    Build Environ

    (2017)
  • H. Shetabivash

    Investigation of opening position and shape on the natural cross ventilation

    Energy Build

    (2015)
  • J. Yu et al.

    Low-energy envelope design of residential building in hot summer and cold winter zone in China

    Energy Build

    (2008)
  • S. Gou et al.

    Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand

    Energy Build

    (2018)
  • Z. Liu et al.

    Application and suitability analysis of the key technologies in nearly zero energy buildings in China

    Renew Sustain Energy Rev

    (2019)
  • A. Ramallo-González et al.

    New optimisation methodology to uncover robust low energy designs that accounts for occupant behaviour or other unknowns

    J Build Eng.

    (2015)
  • X. Chen et al.

    A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios

    Appl Energ

    (2017)
  • R. Singh et al.

    Uncertainty and sensitivity analyses of energy and visual performances of office building with external venetian blind shading in hot-dry climate

    Appl Energ

    (2016)
  • J. Rouleau et al.

    Robustness of energy consumption and comfort in high-performance residential building with respect to occupant behavior

    Energy.

    (2019)
  • A. Prada et al.

    On the effect of material uncertainties in envelope heat transfer simulations

    Energy Build

    (2014)
  • X. Chen et al.

    Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings

    Appl Energ

    (2017)
  • I. Korolija et al.

    Regression models for predicting UK office building energy consumption from heating and cooling demands

    Energy Build

    (2013)
  • I. Macdonald et al.

    Practical application of uncertainty analysis

    Energy Build

    (2001)
  • H. Breesch et al.

    Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis

    Sol Energy

    (2010)
  • S. Asadi et al.

    On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design

    Energy Build

    (2014)
  • W. Tian

    A review of sensitivity analysis methods in building energy analysis

    Renew Sustain Energy Rev

    (2013)
  • G.J. McRae et al.

    Global sensitivity analysis—a computational implementation of the Fourier amplitude sensitivity test (FAST)

    Comput Chem Eng

    (1982)
  • J.C. Helton et al.

    Survey of sampling-based methods for uncertainty and sensitivity analysis

    Reliab Eng Syst Safe

    (2006)
  • A. Prada et al.

    On the performance of meta-models in building design optimization

    Appl Energ

    (2018)
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