Applicability of different extreme weather datasets for assessing indoor overheating risks of residential buildings in a subtropical high-density city
Introduction
In recent years the impact of climate change on the built environment is becoming more evident due to the increasing occurrence and severity of extreme weather events, such as heatwaves, floods, and droughts [1]. These extreme events have devastating effects on the society, human health, and infrastructure systems [2]. Given that most people spend more than 90% time indoors [3] and that buildings play one of the most important roles in urban heat resistance (by providing essential shelter for residents) [4], robust assessments are required to check their resilience to more intense and frequent extreme heat events. The climate resilience and passive survivability of buildings, which refers to a building's ability to maintain safe indoor temperatures in the absence of air-conditioning (AC) [5], have stirred research interest worldwide [6,7]. This is of particular concern to residents with limited capacity to operate an AC system, and during summertime power outages or when energy systems are overtaxed in unexpected weather conditions for which existing building systems were not designed. Particularly in the current unforeseen COVID-19 pandemic, people are spending more time indoors and the importance of diluting pollutants achieved by good natural ventilation has been brought to the public's attention [8,9]. Meanwhile, extreme weather conditions imply that a good thermal performance of naturally ventilated buildings will be more necessary in the post-pandemic period [10]. Therefore, it is imperative to examine buildings' overheating thermal performance in the face of more frequent extreme heat events.
Climatic uncertainties, for example, extremely hot events, could put buildings with poor thermal performance at risk of overheating [11]. In particular, a more hostile environment caused by extremely hot events would have the most significant impact on areas with hot summers, where residential buildings are already vulnerable to the risks of overheating [12]. The majority of previous studies on overheating assessments for free-running residential buildings are found in European temperate climates, for example, the United Kingdom (UK) [[13], [14], [15]], the Netherlands [16,17], and Sweden [18], using available datasets of local extreme weather. Few studies [19,20] have been conducted in tropical and subtropical climates, which have more developing regions with hot summer and warm winter climates [21] and hence populations in this regions would be particularly affected by global warming [12]. For example, in subtropical Hong Kong, some 1.49 million people (~21% of the population) are still living below the poverty line [22], and the residential stocks consist of a large number of low-income housing units. According to a survey conducted in Hong Kong on living conditions in public rental housing [23], 17% of living rooms and 22% of bedrooms do not have air-conditioners installed. Furthermore, the elderly, disabled, and chronically ill people who prefer cooling by passive means such as natural or mixed-mode ventilation [24], are significantly vulnerable, along with low-income residents who cannot afford to use AC due to peak electricity pricing [25]. Different housing types and building characteristics can mitigate or exacerbate occupants’ indoor heat exposure [26], hence the thermal performance of different residential building types in Hong Kong under extremely hot weather conditions is a topic worthy of investigation.
For building performance simulation (BPS), current building designs and evaluation practices usually use the Typical Meteorological Year (TMY) [27], the Weather Year for Energy Calculations (WYEC) [28] and the Test Reference Year (TRY) [29] weather files as the input climate data. These datasets represent the average climatic conditions based on 15–30 years of historical hourly data but do not consider the uncertainties of extreme weather conditions and the future changing climate. To overcome this limitation of the typical year weather data, the Design Summer Year (DSY) was first introduced in the UK to represent near-extreme weather conditions for assessing overheating risks of natural ventilated and mixed-mode buildings in the summer months [30]. Additionally, in 2013, Watkins et al. [31] proposed an alternative approach to construct a new type Design Reference Year (DRY), the DRY is based on individual months according to monthly mean dry-bulb temperature, relative humidity, and global horizontal irradiance. However, both the DSY and DRY have been criticized for poor representativeness and inconsistency with the corresponding TRY [32]. To overcome these shortcomings, in 2015 Jentsch et al. [32] developed the Summer Reference Year (SRY) by adjusting the typical weather year, TRY, to represent a more extreme summer weather condition for BPS. As the SRY was found to incorporate the high dry-bulb temperature reasonably well, it has proved more useful than the TRY in identifying severe overheating risks. More recently, Crawley and Lawrie [33] proposed a method to develop the Extreme Meteorological Year (XMY) weather files by selecting more extreme months with the highest and lowest daily or hourly average dry-bulb temperature to represent site-specific extreme climates that buildings could experience. They reported that the XMY with hourly maximum and minimum dry-bulb temperatures could best capture the range of energy load for buildings’ heating ventilation and air-conditioning systems.
However, the selection of extreme situations is generally based on outdoor meteorological variables for these datasets. To account for indoor extreme events, Guo et al. [34] developed a method to construct Typical Hot Years (THYs; e.g., the Typical Hot Years-Events (THY-E) and the Typical Hot Years-Intensity (THY-I)), for BPS using simulated indoor data. The THYs weather files are defined based on the simulated indoor heat event intensity and are more focused on building performance during extreme heat events. Apart from these widely used extreme weather datasets, there are still other newly developed weather datasets that consider untypical [35] and future extreme weather conditions [36,37]. The constructing methods of the aforementioned widely used extreme weather datasets has been summarized in Table 1.
Above all, it can be seen that with regard to the definition of extreme conditions and the methodology to generate the dataset differ for each type of weather data, there is no consensus on which weather dataset is more appropriate and robust for assessing indoor overheating risks of residential buildings, especially in a subtropical high-density city. Although some studies [33,38] have discussed the impact of one single extreme climatic condition on the indoor thermal environment or building energy demand, there is a lack of comparative work examining the applicability and limitations of various extreme weather datasets for assessing the overheating risks of residential buildings in subtropical climates. Therefore, to fill this knowledge gap, this study is one of the first to compare the most popular extreme weather datasets, namely the THY-E, THY-I, SRY, and XMY, with the TMY for indoor overheating risk, providing insight into which extreme weather datasets can fully represent the extreme weather boundaries for assessing overheating risks of different residential building types in a subtropical high-density living environment. The results will inform architects and building engineers in other similar subtropical cities on the selection of the most appropriate extreme weather dataset for robust building assessment with various purposes. As for local interest, the results presented here will be helpful in understanding the difference of climate resilience and passive survivability between typical Hong Kong residential building types and in the formulation of action plans for designing more resilient buildings to combat the effects of climate change.
This paper is structured as follows. The methods and datasets for development of extreme weather datasets, selecting typical residential building types in Hong Kong, field measurement for building simulation calibration, and indoor overheating assessment criteria are described in Section 2. Section 3 presents the results of overheating risks using the defined static and adaptive overheating thresholds in different residential building types in Hong Kong. This is followed by a discussion on the applicability and limitations of different weather dataset and performance comparison between different residential building types in Section 4. The major findings are concluded in Section 5.
Section snippets
Methods and datasets
In this section, the methods and datasets which are used in this study are discussed as following: (1) Different extreme weather datasets were first constructed in Hong Kong based on the recorded multiple-years weather data, (2) Then, six typical high-density residential building types were selected as residential building “archetypes” in Hong Kong, (3) The outdoor and indoor thermal environment of typical building types had been simultaneously measured for calibration, (4) Temperature in
Overheating risks using static extreme event thresholds
According to the occupancy profile of different rooms, the occupied hours 08h00 to 23h00 and 23h00 to 07h00 were included for assessment of the overheating risks above static extreme thresholds of Tl in the living rooms and bedrooms, respectively. The HI and L of heat events for six archetypes were compared between different weather conditions (see Table 6). It can be seen that the impact of extreme weather conditions on the indoor HI varies across the different weather datasets and a
The applicability and limitations of different weather datasets
The findings in Section 3 illustrate the importance of considering the robustness of the building thermal performance against different weather boundaries. On the one hand, the results show that the most severe HI and He in the daytime for most of the modeled archetypes can be identified when using the THY-I, while the most severe nighttime HI and He can be highlighted by the XMY. On the other hand, the longest daytime L for a heat event can likely be found when using the THY-E weather dataset.
Conclusion
This comparative study investigated the applicability of four approaches to develop extreme weather datasets for assessing overheating risks during hot and humid summers. After the calibration of building simulation models, overheating risks in six residential archetypes in subtropical Hong Kong under different extreme weather conditions were quantified by static extreme event thresholds and the adaptive TM52 approach. According to the results, different weather datasets have their unique
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.
Acknowledgement
This work is fully supported by the Vice-Chancellor's Discretionary Fund of the Chinese University of Hong Kong and the Research Impact Fund (Ref: R4046-18F) of Research Grant Council, Hong Kong.
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