An optimised window control strategy for naturally ventilated residential buildings in warm climates

https://doi.org/10.1016/j.scs.2020.102118Get rights and content

Highlights

  • Presentation of a window control strategy for a naturally ventilated residential building.

  • Evaluation of the window control strategy by comparisons among different control procedures.

  • Control hysteresis is minimised by introducing the outdoor temperature prediction.

  • Strategy with flexible window opening percentage modulation is more effective for indoor thermal comfort maintaining.

  • Introduction of the predictive and feedback control is helpful for avoiding over-ventilation.

Abstract

The adopted window control strategy within an indoor environment affects ventilation performance and occupant comfort. A natural ventilation strategy with optimised window control is proposed and evaluated on a typical Australian residential building. The three main steps of window control are: 1) prediction of outdoor air temperature through rolling forecast; 2) determination of ventilation mode through the decision tree method and; 3) optimisation of window opening percentage through heuristic control. Three control strategies are investigated and compared throughout the hot season. The results suggest that introducing prediction of outdoor temperature into the natural ventilation control process minimises the negative impact of control hysteresis. By using flexible degrees of window opening, the proposed strategy shows improved performance for maintaining an indoor operative temperature compared to the original open-closed control. The mean absolute deviations of indoor operative temperature from the neutral operative temperature are reduced by more than 30 %. In addition, the presented feedback and predictive control logic results in greater stability of indoor thermal operative temperature by avoiding over-ventilation.

Introduction

As a method that requires no energy consumption, natural ventilation can greatly improve the energy efficiency of a building and indoor air quality. Natural ventilation is suitable for both cold and warm climates during the hot and transition seasons and throughout the year in hot climates (Chen et al., 2018). Depending on the speed and high frequency fluctuation of wind (Yan, Li, & Ito, 2019), natural ventilation has the potential to meet both the psychological and physiological needs of building occupants (Ouyang et al., 2006). Therefore, natural ventilation should be the preferred air exchanging approach for buildings. However, earlier studies (Artmann, Manz, & Heiselberg, 2007; Birtles, Kolokotroni, & Perera, 1996; Blondeau, Spérandio, & Allard, 1997; Geros et al., 2005) considered natural ventilation to be an uncontrollable passive and auxiliary method for environmental conditioning such as night-time cooling.

Due to its dependence on differences in indoor and outdoor temperatures and pressures, uncontrolled natural ventilation cannot constantly maintain acceptable levels of indoor air quality and thermal comfort (Chenari, Carrilho, & Silva, 2016; R.G. Southall, 2018). Accordingly, an overall consideration of all impact factors is required in natural ventilation design (R. Southall, 2018). Many past studies have focused on optimised natural ventilation design considering synergistic and antagonistic effects of variables such as building morphology, internal layout and facade features. Guo, Liu, and Yuan (2015) focused on the influences of site planning, building shape and building envelope on natural ventilation efficiency in a specified green building based on the flow field results of computational fluid dynamics (CFD) predictions. Rigorous design of site planning, building shape and building envelope could result in a clear improvement of indoor air flow rate. Ahmeda and Wongpanyathaworn (Ahmed & Wongpanyathaworn, 2012) investigated the effects of opening configurations and sizes on indoor temperature and concluded that adjusting opening positions could result in improved indoor thermal comfort. Stavrakakis et al. (2012) proposed a computational method for window design optimisation in a naturally ventilated, single roomed building of a rural type with different window-to-door configurations and building orientations and concluded that two door-type openings coupled with wind in south and south-east directions could improve the indoor environment for the occupant in standing-seated and standing-recumbent positions. Omrani et al. (2017) found that the addition of a balcony improved and worsened the ventilation performances of single-side ventilated and cross-ventilated residential buildings, respectively. These previous studies indicate that appropriate optimisation of the configurations of building openings can significantly improve the indoor environment under natural ventilation.

With the advent of building management systems (BMSs), motorised windows have been widely implemented in smart buildings (Ren & Cao, 2020; Yin et al., 2010). De Dear (De Dear & Brager, 1998) concluded that the thermal sensations and perceptions of occupants could be altered by the control of building openings under certain weather conditions. Therefore, appropriately designed automatic window control algorithms can also improve the indoor thermal environment (Lenoir, 2013). There have been recent efforts to develop natural ventilation control strategies based on indoor thermal comfort, such as the development of an automatic window opening system by Mochida et al. (2005) based on the predicted mean vote (PMV) model. In the system, windows were opened and closed under a PMV greater than 0.1 and less than −0.1, respectively. An empirical model based on the statistical data obtained from a set of in situ measurements in a typical office was proposed by Ardeshir and Claus (Mahdavi & Pröglhöf, 2008) to predict the rate of indoor air change and the mean air velocity under different window opening positions. The Energy in Buildings and Communities Programme (EBC) Annex 62 project gathered information for a large number of well documented case-study buildings worldwide and presented suitable design methods and tools for the prediction of cooling requirements, ventilative cooling performance and risk of overheating (Annex, I., 62, 2013; O’Sullivan et al., 2017). By studying manual and automated window control strategies, Psomas et al., (2016) determined the discharge coefficient and opening position of windows to be critical variables determining the effectiveness of ventilative cooling strategies in Copenhagen, Denmark during the hot season. Peuoportier and Duer (Peuportier et al., 2013) presented a field monitoring and modelling approach for the evaluation of ventilative cooling based on a single family house in Paris, France. Belleri et al. (Belleri et al., 2017) using a case-study commercial building in Norway proposed a cooling strategy which combines the effect of opened sliding doors and skylight apertures to enhance stack ventilation. By simulating thirteen natural ventilation control settings with different opening configurations, Barbosa et al., (2015) found that double-sided ventilation with constantly fully opened apertures achieved the best performance in avoiding overheating and minimising uncomfortable indoor temperatures. Yin and Wang (2015) presented a rule-based control strategy to estimate the natural ventilation potential in humid continental, humid subtropical and Mediterranean mild climates. Their strategy worked by regulating window opening percentage based on a linear function relating indoor and outdoor air temperatures. Although the implementation of various window opening controls resulted in improvement of indoor environments under all the aforementioned studies, none of the studies managed to modulate the timing of opening controls to offset the negative influence of outdoor climate on indoor thermal comfort. Consequently, there were periods during which indoor thermal comfort requirements were not met in these studies.

The current study presents an optimised window control strategy to achieve effective natural ventilation and improved indoor thermal environment. The strategy aims to be predictable by combining numerical simulation, heuristic decision making and feedback control. To evaluate the performance of the strategy, numerical simulations based on multi-zone network and computational fluid dynamics (CFD) are performed. The research presented can provide valuable information for improving the utilisation of natural ventilation.

Section snippets

Building model

For the evaluation and development of an improved window control strategy, a representative model based on a typical Australian single-story multi-zone residential building was adopted. The five rooms in this model are a master bedroom, a guest room, an open plan living room, a laundry space and a bathroom. As a north-facing building, the living room and master bedroom occupy the north side whereas the guest room is located at the southeast corner (Fig. 1). All rooms and openings are located at

Outdoor air temperature

Table 4 shows the satisfying natural ventilation hour (SNVH) required during hot weather and quantifies the influence of predicted outdoor air temperature on the utilisation of natural ventilation. The proposed SNVH is defined as the total hours of a typical season (i.e. 2,190 h) during which indoor operative temperatures under natural ventilation conditions meet thermal comfort requirements.

The results showed that the SNVHs of all rooms were greater under prediction of outdoor air temperature

Conclusions

The current study proposes an optimised natural ventilation strategy employing predictive and feedback control based on a residential building model. Under the strategy, building windows are modulated to open to a varying percentage (from 0 % to 100 %) based on indoor operative temperature. The presented approach combines prediction of outdoor air temperature, determination of ventilation mode and optimisation of window opening percentage. Combined numerical simulations were performed for

Declaration of Competing Interest

The authors declare that there are no conflicts of interest.

Acknowledgements

The authors would like to acknowledge the financial support provided by the Natural Science Foundation of Shaanxi Province of China (Project ID: 2018JQ5207). Dr. Zhenjun Ma is thanked for providing assistance with the experiments.

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