Language：Japanese   Publisher：E3S Web of Conferences  

Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate.

DOI： 10.1051/e3sconf/202339605001

Scopus

Language：Japanese   Publisher：E3S Web of Conferences  

Solar radiation information is very important in green building design, namely for the daylighting, solar heat gain prevention as well for solar energy utilization. This study uses solar radiation data from six pyranometer sensors for measuring the horizontal component (global horizontal irradiance (GHI) and infrared irradiance) and the vertical component (pyranometer sensors to the north, south, east, and west) located in Tangerang, Indonesia. Measurements have been filtered for one year from January 2021 to December 2021. The solar radiation observation is complemented with other measurement of climate elements, such as wind speed and direction, dew point temperature, relative humidity, and air temperature. The diurnal and seasonal patterns of solar irradiance and other climatic elements have been observed using the visualization technique of heat maps. The results show that sensors pointing north experience an increase in solar intensity in May, June, July, and August. Meanwhile, sensors that point to the south experience the increases in solar intensity in November, December, January, and February. The increase in radiation intensity towards the east occurs from 07.00 to 10.00 local time (LT), while the increase in sensor intensity towards the west occurs from 14.00 to 16.00 LT. The results of solar radiation analysis in horizontal and vertical surfaces are combined with other climate elements to create a bio-climatic design guideline suitable for buildings in the hot and humid climate.

DOI： 10.1051/e3sconf/202339605002

Scopus

Language：Japanese   Publisher：Climate  

This study evaluates the performance of 6 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating temperature, precipitation, wind speed, and relative humidity over 29 cities in Indonesia. Modern-Era Retrospective Analysis for Research Applications (MERRA-2) was considered as reference data to assess the city-wise performance of surface air temperature, precipitation, wind speed, and relative humidity simulated by the CMIP6 GCMs during 1980–2014. Six statistical measures were computed in this process (mean annual, seasonal amplitude, mean annual bias, root mean square error, correlation coefficient, and standard deviation). For 29 cities, the mean annual values of surface air temperature, precipitation, wind speed, and relative humidity obtained from the GCMs range between 290 to 302 K, 100 cm to 450 cm, 1 to 6 m/s, and 70 to 94%, respectively. The correlation coefficient between the GCMs and the surface air temperature (precipitation) reanalysis dataset ranges from 0.3 to 0.85 (−0.14 to 0.77). The correlation coefficient for wind speed (relative humidity) varies from 0.2 to 0.6 and is positive in some cases (0.2 to 0.8). Subsequently, the relative error that combines the statistical measurement results was calculated for each city and meteorological variable. Results show that for surface air temperature and precipitation, the performance of TaiESM was outstanding over the 10 or more cities. In contrast, for wind speed and relative humidity, NOR-MM and MPI-HR were the best over 7 and 19 cities, respectively. For all the meteorological variables, the performance of AWI was found to be worst over all the cities. The outcomes of this study are essential for climate-resilience planning and GCM selection while performing downscaling experiments. It will also be useful for producing updated national climate change projections for each city in Indonesia and providing new insights into the climate system.

DOI： 10.3390/cli11050100

Scopus

Language：Japanese   Publisher：Building and Environment  

The classification of climate zones is required for developing appropriate predesign strategies for energy-efficient buildings. Standardized hourly climatic data from 2014 to 2020, including global horizontal irradiance, air temperature, wind speed, relative humidity, precipitation, atmospheric pressure, total cloud cover, and mixing ratio, collected from 106 sites were used for climate zoning in Indonesia. First, the temperature zones containing four area divisions were determined to obtain statistical patterns of diurnal and seasonal air temperature. The comfort ventilation potential was assessed based on a zoning using a combination of air temperature and wind speed characteristics, while the evaporative cooling potential was assessed based on the combination of relative humidity and wind speed characteristics. Total hours for comfort ventilation and evaporative cooling showed diurnal (daytime and nighttime) and seasonal changes. Second, eight integrated climate zones namely, equatorial, sub-equatorial, highland tropical, very highland tropical, monsoonal, sub-monsoonal, savanna, and sub-savanna, were determined based on principal component analysis, cluster analysis, and spatial interpolation methods. Third, the calculation of cooling degree days and cooling loads followed by the calculation of the potentials for night ventilation, comfort ventilation, and evaporative cooling were carried out in each integrated climate zone. For example, by taking the lower probability limit of 50%, the monsoonal, savanna, and sub-savanna climate zones were found to be suitable for comfort ventilation methods. Meanwhile, the sub-equatorial climate zone was suitable for applying passive methods by combining night ventilation and comfort ventilation.

DOI： 10.1016/j.buildenv.2022.109698

Scopus

Language：Japanese   Publisher：Architectural Institute of Japan  

<p>In recent years, passive designs, that acquire solar radiation from large windows during winter time, are becoming widespread. However, due to privacy issues, there are some houses in which curtains and other shading devices remain closed throughout the year. In this research, we focused on the roller blinds that can control the solar radiation and view, in response to the weather conditions and the surrounding environment, and measure the solar heat gain and the heat transfer by component of the windows with roller blinds under the actual environment.</p>

DOI： 10.3130/aijt.28.745

Event date： 2021.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.9

Language：Japanese   Presentation type：Oral presentation (general)