著者

有馬 雄祐

出版者

日本建築学会

雑誌

日本建築学会環境系論文集 (ISSN:13480685)

巻号頁・発行日

vol.86, no.785, pp.680-691, 2021-07-30 (Released:2021-07-30)

参考文献数

48

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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.

著者

有馬 雄祐 大岡 龍三 菊本 英紀

出版者

日本建築学会

雑誌

日本建築学会環境系論文集 (ISSN:13480685)

巻号頁・発行日

vol.81, no.729, pp.1047-1054, 2016 (Released:2016-11-30)

参考文献数

21

被引用文献数

2

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The outputs of weather and climate models have been used in various application fields. For example, future weather data for the building energy simulation (BES) can be provided based on a climate model prediction. However, as the model output has systematical errors (called the bias), some type of bias correction is necessary in order to use the model output for an application field. For temperature or humidity, we often assume normal distribution and correct bias using statistical parameters, such as the average and the standard deviation. However, for solar radiation, a bias correction method (BCM) that uses only the average and standard deviation is insufficient and can result in negative values after bias correction. Consequently, the solar radiation bias is often corrected using only its average. In general, climate models can accurately predict the daily maximum amount of solar radiation on clear days at a given site because solar radiation depends mainly on its geolocation (latitude, longitude, and elevation) and the season (solar altitude). However, it is difficult to model cloud physics processes accurately to establish the weaker amounts of solar radiation on cloudy days. As a result, when we correct the solar radiation bias using only the average value, the daily maximum value deviates from the observed results instead of correcting the average. In this paper, we present a method called quantile mapping (QM) for the bias correction of solar radiation to provide the bias corrected weather data for the BES. The QM has been developed mainly for the correction of precipitation or temperature biases, although there are few studies that apply QM to the correction of solar radiation. In previous studies, QM was applied to the daily or monthly average. However, for the BES, the daily maximum value is also as important as the daily or monthly average, because the peak energy load depends mainly on the daily maximum. In this study, we also applied QM to obtain the daily maximum amount of solar radiation. In addition, we conducted BESs using the bias corrected weather data and evaluated the efficiency of each BCM. From the simulation results, the average energy consumption did not differ according to the difference in the BCM. However, the simulation that used the weather data corrected by only the monthly average could not predict the maximum cooling load; it was underestimated by 12%. Conversely, the simulation with the data corrected by QM, which used either the daily cumulative or the maximum amount of solar radiation, could predict the maximum cooling loads, which were under estimated by only 6% and 2%, respectively.

著者

有馬 雄祐 大岡 龍三 菊本 英紀 山中 徹

出版者

東京大学生産技術研究所

雑誌

生産研究 (ISSN:0037105X)

巻号頁・発行日

vol.66, no.1, pp.61-68, 2014-01-01 (Released:2014-03-18)

参考文献数

21

被引用文献数

1

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地球温暖化など気候変動が進んでおり,建築は気候から多大な影響を受ける.気候に適した設計を行うために,気温や日射などの気象要素から成る標準気象データを用いた熱負荷計算が行われる.現在は,各地域の過去の観測値を基にして作成された標準気象データを使用することが一般的である.しかし,建築物は長期にわたり使用され,その間に気候は変動する.そのため,将来の気候へ適応した,長期的な省エネを実現する建築設計のためには気候変動を考慮した熱負荷計算が必要である.そこで本研究では,GCMの予測する気象データを領域気象モデルによって力学的ダウンスケーリングを行い未来の標準気象データの作成する.