The presentation technology about architectural design has been developed in recent years. Virtual Reality as one of the newest method which was born from the modern technology, has been used at practical design. In addition, some of the universities had set up VR laboratories for design education. Compared to the traditional method using 2D drawings and models for presentation, VR will be expected to understand the space more easily without specialized knowledge, and it will be more useful for improving the designers’ ability.
Therefore, in order to grasp the designers’ awareness about the applicability of VR Space, we conducted a hearing survey with 12 university professors in Chugoku region who worked in the field of architectural design or design education. According to the results, many of the professors thought “Scale feeling” was “the ability to measure the distance with the eyes”, and they had negative viewpoints about to learn “Scale feeling” in VR space, such as “It is difficult to understand the distance in VR Space” or “Drawing by hand is more useful to nurture the scale feeling than VR”. Therefore, this research was focused on the “Scale feeling”, and conducted an experiment to verify the effectiveness of VR by comparing the learning effect about “Scale feeling” in “VR Space” with “Real Space”.
The procedure of this experiment, first, for grasping the original “Scale feeling” of subjects, the subjects were asked to answer the length of 15 objects in Test Space. Then, the subjects were divided into two groups to learn “Scale feeling” in two types of learning spaces. One group learned in “VR Space” and another group learned in “Real Space”. After learning, the subjects came back to “Test Space” and answered the length of 15 objects as the same manner at first. The difference between the “Answered length” and “Actual length” in “Test Space” was defined as the “Inaccuracy”.
Finally, we compared the learning effects by the average and the standard deviation in “Inaccuracy” between the two groups. The degree of “Inaccuracy” reduction was similar. Therefore, the “Scale feeling” can be developed in VR space as same as Real space. Although, in “VR space”, the objects which have no actual shape have a larger standard deviation of the “Inaccuracy” than “Real Space”, we considered that it’s more difficult to grasp the distance between objects without shape in “VR space”. For the future research, we will consider in more detail based on the visual characteristics of the learning method.
Studies about well-being (WB) have been used to build an interdisciplinary research area centered on positive psychology and economics of happiness. WB research is characterized by active use of subjective data about one’s life, called subjective wellbeing (SWB), as indicators of the quality of an individual’s life or their society. SWB has various domains, including ones related to cognitive and emotional well-being, and each has different determinants. For example, life satisfaction, which is the cognitive aspect of SWB, is strongly correlated with income, while emotional well-being has a relatively strong correlation with health and social relationships. There are various theories about SWB’s composition, and the OECD has defined three basic domains of SWB: life evaluation (life satisfaction), emotion (affect, mood), and eudaimonia. Conventional research for assessing residential environments has used “housing satisfaction” as a subjective indicator of housing quality, which belongs to the “cognitive evaluation” domain. However, based on the findings of WB research, it can be inferred that there are diverse subjective domains related to housing quality. Therefore, in the current study, we attempted to construct home-related subjective well-being (HOME-SWB) based on the OECD’s SWB definition: “home satisfaction,” “positive emotions at home,” “negative emotions at home,” and “eudaimonia derived from home.” “Home satisfaction” is the cognitive aspect of HOME-SWB, which is similar to the conventional subjective indicator, housing satisfaction. “Positive emotions at home” includes the frequency of positive emotional experiences at home, such as feeling happy, cheerful, or joyful, while “negative emotions at home” includes the frequency of negative emotional experiences, such as feeling depressed, stressed out, or lonely at home. “Eudaimonia derived from home” indicate to what extent residents obtain experiences of eudaimonic well-being from their homes, such as self-esteem and the sense that life is worth living. The purpose of this study is to investigate the current status of HOME-SWB among 4,000 residents in the Tokyo area and the determinants of each domain of HOME-SWB using ordinary least squares (OLS) regression analysis.
Our assessment shows that HOME-SWB is closely related to demographics; for example, the relationship between home satisfaction and age is a U-shaped curve, which is similar to the well-known relationship between life satisfaction and age. Therefore, we conducted OLS regression by controlling demographic variables, including gender, age, and household income, and the results show that each domain of HOME-SWB has unique relationships with them. For example, the size of a house strongly affects home satisfaction but not positive emotions or eudaimonic aspects. Having a nice view from windows or a high level of thermal insulation has a relatively strong effect on emotional HOME-SWB. Proactive ways of living in a home, such as being picky about furniture and the interior of one’s home or frequently redecorating rooms, enhance the eudaimonic aspects, such as self-esteem and optimism. When we use conventional subjective information to measure housing satisfaction as an indicator of housing quality, it is noted that the importance of the housing elements that strongly affect cognitive well-being, such as the size of a house, are overestimated, while the importance of elements that have an impact on the emotional and eudaimonic aspects of HOME-SWB are underestimated. There are various subjective domains related to housing quality; therefore, we can conclude that we must measure various domains of HOME-SWB when assessing home-related well-being based on residents’ subjective information.
According to statistical data published by the Japanese government, the major causes of death in Japanese people are cancer, heart disease and cerebrovascular disease. The incidence of cerebrovascular disease in particular trends to be higher during winter than summer. One possible reason for this seasonal difference is that exposure to low temperatures can cause fluctuations in blood pressure. In houses with poor thermal insulation, indoor temperature differences between heated and non-heated spaces, such as the bathroom, corridors, and lavatory, can be considerable in winter. Many houses in the Tohoku region have a poor thermal environment in winter, and the incidence of cerebrovascular disease in this area is the highest in Japan. Hasegawa and Yoshino investigated the association between the indoor thermal environments of houses in Yamagata Prefecture, which is located in the Tohoku region, and the death rate due to cerebrovascular disease in the winter of 1983 and 1984. They found marked temperature differences between heated living rooms and unheated rooms. In addition, the occupants of houses with unheated lavatories and/or bedrooms were significantly more susceptible to cerebral vascular accidents.
In order to clarify the association between the indoor environment of residential buildings and cerebrovascular disease, an epidemiological survey was therefore conducted in Yamagata Prefecture. The areas investigated included three of the rural towns that were surveyed in the study conducted approximately 30 years ago. The study was divided into three phases. The first phase (Phase 1) comprised a cross-sectional questionnaire on the indoor thermal environment and occupants' lifestyle habits, and was administered to 188 elderly persons. The second (Phase 2) and final (Phase 3) phases comprised field measurements of the indoor thermal environments and home blood pressure measurements of subjects selected from the Phase 1 part of the study, respectively. This study describes the results obtained from indoor temperature and blood pressure measurements of 55 elderly persons over an approximately 18-month period in the study area. The association between blood pressure and indoor temperature exposure in these elderly persons is analyzed statistically.
In almost the houses surveyed, the living room temperature was maintained at approximately 18℃ after dinner. However, once the heater was turned off, the room temperature decreased rapidly overnight and approached outdoor temperatures by daybreak. The bedroom and washroom temperatures were similar to those outdoors throughout the day. The occupants of such houses were therefore exposed to marked temperature differences whenever they left the living room while the heater was being used.
Although the systolic blood pressure of the elderly persons surveyed increased as outdoor temperatures decreased, this tendency was not statistically significant. However, when indoor thermal differences were examined, the systolic blood pressure of elderly persons was significantly increased when the indoor temperature dropped below 15℃ (p<0.01, Friedman's test). The blood pressure difference observed when indoor temperatures were below 10℃ compared to that observed above 15℃ was 10 mmHg. In addition, when elderly persons were exposed to indoor temperatures below 15℃, systolic blood pressure reached levels as high as 140 mmHg, implying that the systolic blood pressures of elderly persons increased at indoor temperatures below 15℃.
Since typical interior materials have high-emissivity surface of about 0.9, far-infrared generated from radiant heater is absorbed once in interior walls and then reradiated infrared from the warmed walls reaches human body surfaces. In the case of low-emissivity (low-E) interior surface of about 0.1, generated far-infrared is reflected at interior wall surface and reaches human body surfaces without warming processes of walls. Thus, it is expected that human body feels warmth quickly and/or heating load can reduce owing to the decrease in set temperature by forming the room covered with low-E materials. Therefore, we studied effects of the low-E interior surface material on interior thermal environment using radiant heating by comparing two real-size experimental rooms with the same thermal insulation: one is covered with a low-E material, aluminum foil (Low-E room), and the other is covered with a normal interior material (Std-E room).
When warm up the rooms using electrically heated carpet as radiant heating equipment, the globe and air temperatures of the Low-E room increased rapidly and showed about 0.8℃ higher temperatures than those of Std-room in 30 min. The radiant temperatures of the wall and ceiling of Low-E room increased immediately with the increase in the temperature of the carpet by turning on, while slow increase in those of Std-E room. Thus, the increase in globe and in air temperatures of the Low-E room may be because the whole Low-E surface, except for floor, of the room become pseudo radiant heating surface.
In this study we used aluminum foil as low-E materials. However, this material is not suitable for interior due to high specular reflection. Therefore, novel materials with low specular reflection in the visible range and low emissivity in the far -infrared range are also developing at present.
Earth-to-air heat exchangers (EAHE) utilize the heat capacity of soil to pre-cool or pre-heat outside air (OA), and then introduce OA into the air handling unit to reduce the heat load of OA. However, at the operational phase, the control of an EAHE is not optimized (e.g., inefficient energy saving or lower quality of introduced air). It is also difficult to establish the optimal control rule that considering the future effects by sequential operations since the control response takes extremely long time. In this study, we focused on the reinforcement learning (RL) control, which does not require the construction of control rules. In RL, an agent learns to maximize reward in the state s within an environment, and this algorithm achieves its purpose by passing action a that maximizes reward to the environment. One of the advantages of RL is maximizing the cumulative reward instead of maximizing the sequential reward. For this reason, RL is suitable for unsteady problems.
The purpose of this study is to establish the optimal control rules for an EAHE by RL. In this paper, we validate the RL control rule that achieve the two objectives in which decreasing the heat load of the fresh air handling unit (FAHU) and suppressing the occurrence of condensation in the EAHE. Firstly, we define the control problem for RL using the environment estimated by long-term performance prediction method of EAHEs based on CFD developed by authors. Then, we incorporate training logic to the environment and conduct the training of RL. Secondary, we implement the algorithm for efficient learning by defining rewards as the results of factual and counterfactual actions. Then, we verify the effectiveness of the RL control by comparing the RL control with the scheduled control, etc. Finally, we analyze the tendency of actions selected by RL.
The reward is defined by a method of feeding back to the actions selected by the Agent to the relative rewards. This method compares and evaluates not only the results of factual action but also the results of counterfactual action based on the Counterfactual Predictor. This makes it possible to give immediate feedback to the Agent which action was appropriate. We call this method Factual and Counterfactual Reward Estimation (FCRE).
As a result of RL using FCRE, it is possible to obtain rough policy in the early stages of the learning and high convergence of learning. Comparing the RL control with the scheduled control, heat load of FAHU is increased about 3%, but the occurrence of the condensation is extremely decreased by the RL control. It was confirmed that it is possible to control to achieve the above two objectives simultaneously by the RL control.
Following the Paris Agreement adopted in 2015, Japan has set a goal of reducing greenhouse gas emissions by 26% from 2013 by 2030. In this context, the business sector is required to reduce greenhouse gas emissions by 40%. The government has been promoting energy saving by some measures. Predicting the effects of each energy-saving policies will make it possible to consider a more effective combination of policies, and it will be useful for studying future policy making. In this study, we developed a simulation that modeled the behavior of tenants and building owners and we used the simulation to predict the effect of energy saving policies on buildings. We also examined changes in energy consumption when tenants prefer energy-efficient buildings. Chapter 2 explained a model in detail. This is a Multi-Agent Simulation model that simulates the behavior of building owners and tenants, who are stakeholders of office buildings. One thousand building owners and about 10,000 tenants act in order to satisfy their own profits. In particular, building owners change their rent, reconstruct and renovate so that they can get more their lifecycle profits. If they think that energy-efficient building is profitable, they change their buildings to the green ones. On the other side, tenants often move to a new office in order to satisfy their utility. If tenants prefer the energy-efficient office, their intention affect the owners’ decision making indirectly. Parameters of utility are estimated so that calculated rental value of buildings simulates the actual value. Using this simulation, we predicted the policy effects of energy saving in building sector from 2020 to 2050 in Chapter 4. The considered policy is “energy-efficiency standard compliance”, “subsidy for new buildings” and “subsidy for renovation”. As a result, we knew some important findings. For example, making energy-efficiency standard compliance severe is effective. It was also found that subsidies for new buildings require a large amount of subsidies to achieve an effect because building a new office requires a lot of money and little amount of subsidies cannot distribute properly to new buildings. Furthermore, we found that subsidies for renovation can be effective at an early stage, but the effects will not continue over the long term because renovated buildings are often reconstructed to the new buildings and the effects of subsidy will end at that time. In addition to those policies, we studied the scenario the tenant's intension increases to save energy consumption in Chapter 5. As a result, tenants’ intension to energy-efficiency makes energy consumption greatly reduced in the end, but it takes time for the effect to appear. We also studied the combination between tenants’ intention and the policies in Chapter 4. The combination with the standard compliance is not very effective. Otherwise, there were the synergistic effect with subsidies for new buildings. It was also clarified that there is a possibility that energy conservation will continue to progress if properly combined with subsidies for renovation. As a future task, it is conceivable to consider specific measures for tenants to intend energy-saving when selecting a building. However, since this simulation is constructed with limited information and includes assumptions and simplifications, there remains a problem in its prediction accuracy. It is also necessary to consider a method for verifying the prediction accuracy while aiming for a more detailed model construction.