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

The environmental impacts of mechanical, electrical and plumbing (MEP) systems have been largely overlooked and are commonly excluded from building-scale life cycle assessments (LCAs). Understanding the impacts and reduction potential of these systems is crucial for decarbonizing retrofits and new buildings. Therefore, we have conducted a critical review of LCA studies on MEP systems in buildings, selected using a systematic method, to identify: 1) estimates for upfront embodied [A1-A5] and replacement [B4] carbon impacts; 2) LCA reporting fundamentals needed to ensure transparency and interpretability of results; 3) future research directions. Since 2016, 54 studies presented sufficient information to investigate presented methodologies and LCA results of MEP systems. The review reinforces the need to report environmental impacts by individual life cycle stages and building or system elements to interpret influencing factors and enable further utility of results. Two studies did not report the impact assessment method nor background dataset used for the assessment rendering their results incomparable with others and are excluded from analysis. Based on the median (and mean) of the reviewed studies, estimates for A1-A3 and A1-A5 of the MEP systems are 40 (49) and 49 (61) kgCO2e/m², respectively. Additionally, based on reviewed studies, the systems will be replaced at least twice throughout a 60-year reference study period leading to approximately 100 kgCO2e/m² for B4. These values almost certainly underestimate actual impacts due to the incomplete physical scopes spanned by the studies. Studies with more complete scopes generally reported higher values. The variability in scopes covered, reporting practices, and values reported, and the relatively small number of studies found, highlight the need for further investigation and improvement. Ten key research needs are identified, including the impact reduction potential of MEP systems and the influence of system layout and typology on the reported impacts. Additionally, future research should develop system specific benchmarks and reduction strategies while moving away from purely descriptive studies that report environmental impacts using single-point values.

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.

Fine particulate matter (PM2.5) contributes to millions of excess deaths worldwide each year. Wildfires are a major source of PM2.5 and sheltering indoors using central air filtration is effective in reducing indoor PM2.5 levels during an event. We analyzed smart thermostat usage in California homes during the 2020 wildfire season and found that central air is not generally utilized for air filtration. We evaluated the effectiveness of operating central air systems to reduce indoor PM2.5 levels using a mass balance model. Simulation results showed up to a 56% reduction in indoor PM2.5 during wildfire days from central air filtration. Installing a MERV 13 filter, as opposed to a MERV 10 filter, can further lower indoor PM2.5 concentrations by up to 31%. Using central air systems for filtration can match the efficiency of four portable air cleaners in an average Californian home. These results may serve as the basis for an automated control logic to utilize central air filtration during a wildfire event.

American desert cities designed and built at the turn of the century, in collaboration with the advent of air-conditioning technologies, have been able to house millions of Americans by relying primarily on fossil-fuels to supply relief from extreme hot weather. The Phoenix Metro Area, or The Valley of The Sun as it is known to locals, experienced 145 days reaching tempera-tures over 100˚F in 2020 according to the National Weather Service. In July of 2023, Phoenix set a new record with 31 days straight of over 110-degree heat. The increased probability of a longer-lasting heat-wave, combined with the over demand of electrical power supply during extreme weather events can be catastrophic, especially to the most vulnerable communities.2 As climate change intensifies, desert cities like Phoenix must innovate and adapt, ensuring the safety and well-being of all residents, particularly those in high-risk areas.

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.