Department of Architecture

There are three imperial residences in Kyoto: Gosho (京都御所), rebuilt in 1855 and used for formal affairs even today; Shūgakuin (修学院離宮), a summer retreat on mountain slopes built in the mid-seventeenth century; and Katsura Imperial Retreat (桂離宮), slightly older than Shūgakuin. Upon the death of the Hachijō imperial line in 1881, Katsura came into the hands of the reigning household; shortly afterward, the Imperial Household Ministry was formed and took responsibility for the care of such sites. Sometimes grouped with the other residences, Nijō Palace was originally built not for the imperial household but for the warriors who effectively ruled Japan from the seventeenth to the middle of the nineteenth century; today, it too is managed by the Imperial Household Agency (the scope and name of the Imperial Household Ministry having changed at the end of World War II). Of these four, Katsura, with its extensive grounds and esteemed teahouses in addition to a large, shoin-style residence, is best known of all, used both at home and abroad to illustrate arguments about architecture and national tradition. Yet even so, much remains to be said about the complex, as demonstrated by this brief descriptive bibliography. Download High-Resolution PDF

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xian University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants thermal comfort responses.

Elevated air movement produced by fans can offset air-conditioning energy requirements by allowing temperature setpoints to be raised without compromising thermal comfort. These advantages are even greater in hot and humid climates that inherently have large and sustained indoor cooling requirements. Few studies have assessed the in-situ benefits of fans in actual buildings. We installed ceiling and desk fans into a Zero Energy office building (675 m2) in Singapore. Across an 11-week period, 35 occupants alternated between two conditions (no fan vs. fan): 24 °C setpoint with fans off, and 26.5 °C setpoint with fans on. When the temperature setpoint was raised and elevated air movement was provided, a 32% energy reduction was obtained. The energy savings accrued without any negative impacts occurring on thermal satisfaction. Overcooling caused by thermal preference to slightly warmer and warmer conditions was substantially reduced from 33 to 9%. No changes in perceived air-staleness or self-reported alertness and ability to concentrate occurred either, indicating parity across the no fan and fan conditions. Although occupants primarily relied on ceiling fans at the 26.5 °C setpoint, they were by default on at the beginning of each day, giving less incentive to use the desk fans. Our study took place in a high-performance Zero Energy building, whereby thermal dissatisfaction was already low (7%). Therefore, notable changes did not occur, but significant improvements to thermal comfort could still occur in buildings that are unable to maintain high levels of thermal satisfaction.

Daylighting standards provide an assessment method that can be used to evaluate the quality of window views. As part of this evaluation process, designers must achieve five environmental information criteria (location, time, weather, nature, and people) to obtain an excellent view. To the best of our knowledge, these criteria have not yet been verified and their scientific validity remains conjectural. In a two-stage experiment, a total of 451 persons evaluated six window view images. Using machine learning models, we found that the five criteria could provide accurate predictions for window view preferences. When one view was largely preferred over the other, the accuracy of decision tree models ranged from 83% to 90%. For smaller differences in preference, the accuracy was 67%. As ratings given to the five criteria increased, so did evaluations for psychological restoration and positive affect. Although causation was not established, the role of most environmental information criteria was important for predicting window view preferences, with nature generally outweighed the others. We recommend the use of the environmental information criteria in practice, but suggest some alterations to these standards to emphasize the importance of nature within window view design. Instead of only supporting high-quality views, nature should be promoted across all thresholds dictating view quality.

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using two personal comfort datasets containing longitudinal data from 55 people. The datasets comprise more than 6000 unique right-here-right-now thermal comfort surveys. The results show that a CCM that uses only the one-time onboarding survey information of an individual occupant has generally as good or better performance as compared to conventional general-purpose models, but uses no historical longitudinal data as compared to personalized models. If up to ten historical personal preference data points are used, CCM increased the thermal preference prediction by 8% on average and up to 36% for half of the occupants in the first of the tested datasets. In the second dataset, one-third of the occupants increased their thermal preference prediction by 5% on average and up to 46%. CCM can be an important step toward the development of personalized thermal comfort models without the need to collect a large number of datapoints per person.

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using a smartwatch for 180 days. We collected more than 1080 field-based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of the time to want no change in their thermal environment despite completing 75% of these surveys at temperatures higher than 26.6°C. All but one personal comfort model had a median prediction accuracy of 0.78 (F1-score). Skin, indoor, near body temperatures, and heart rate were the most valuable variables for accurate prediction. We found that ≈250-300 data points per participant were needed for accurate prediction. We, however, identified strategies to significantly reduce this number. Our study provides quantitative evidence on how to improve the accuracy of personal comfort models, prove the benefits of using wearable devices to predict thermal preference, and validate results from previous studies.

SARS-CoV-2 has been recognized to be airborne transmissible. With the large number of reported positive cases in the community, home quarantine is recommended for the infectors who are not severely ill. However, the risks of household aerosol transmission associated with the quarantine room operating methods are under-explored. We used tracer gas technique to simulate the exhaled virus laden aerosols from a patient under home quarantine situation inside a residential testbed. The Sulphur hexafluoride (SF6) concentration was measured both inside and outside the quarantine room under different operating settings including, air-conditioning and natural ventilation, presence of an exhaust fan, and the air movement generated by ceiling or pedestal fan. We calculated the outside-to-inside SF6 concentration to indicate potential exposure of occupants in the same household. In-room concentration with air-conditioning was 4 times higher than in natural ventilation settings. Exhaust fan operation substantially reduced in-room SF6 concentration and leakage rate in most of the ventilation scenarios, except for natural ventilation setting with ceiling fan. The exception is attributable to the different airflow patterns between ceiling fan (recirculates air vertically) and pedestal fan (moves air horizontally). These airflow variations also led to differences in SF6 concentration at two sampling heights (0.1 m and 1.7 m) and SF6 leakage rates when the quarantine room door was opened momentarily. Use of natural ventilation rather than air-conditioning, and operating exhaust fan when using air-conditioning are recommended to lower exposure risk for home quarantine. A more holistic experiment will be conducted to address the limitations reflected in this study.

We evaluated diurnal trends of size-resolved indoor and outdoor fluorescent biological airborne particles (FBAPs) and their contributions to particulate matter (PM) within 0.5-20 μm. After a ten-week continuous sampling via two identical wideband integrated bioaerosol sensors, we found that both indoor and outdoor diurnal trends of PM were driven by its bioaerosol component. Outdoors, the median [interquartile range] FBAP mass concentration peaked at 8.2 [5.8-9.9] μg/m³ around sunrise and showed a downtrend from 6:00 to 18:00 during the daytime and an uptrend during the night. The nighttime FBAP level was 1.8 [1.4-2.2] times higher than that during the daytime, and FBAPs accounted for 45 % and 56 % of PM during daytime and nighttime, respectively. Indoors, the rise in concentrations of FBAPs smaller than 1 μm coincided with the starting operation of the heating, ventilation, and air conditioning (HVAC) system at 6:00, and the concentration peaked at 8:00 and dropped to the daily average by noontime. This indicated that the starting operation of the HVAC system dislodged the overnight settled and accumulated fine bioaerosols into the indoor environment. For particles larger than 1 μm, the variation of mass concentration was driven by occupancy. Based on regression modeling, the contributions of indoor PM, non-FBAP, and FBAP sources to indoor mass concentrations were estimated to be 93 %, 67 %, and 97 % during the occupied period.