Diffuse solar radiation converted from global radiation using five splitting models are compared with those obtained by observation. Then, solar radiation on the four vertical walls calculated from direct and diffuse radiations using two synthesizing models are compared with those obtained by observation. The comparison shows that the combination of splitting and synthesizing models developed by Perez et al. is the most recommendable among all combinations.
2010s reference weather data, 17 locations were created. We calculated the heating and cooling load of offices and detached houses using reference weather data and 10- year annual weather data, and compared them. The method of creating reference weather data is reasonable and is close to the average annual heating and cooling load for 10 years at any locations.
The recent 60-year Tokyo meteorological data was prepared for thermal load calculation, and based on this, we have created six reference weather data for every 10 years. Based on the newly created 2010s reference weather data, the annual cooling and heating load were calculated slightly larger than cooling load of the reference weather data for other periods.
The purpose of this study is to clarify the qualities required for BIM(Building Information Modeling) development personnel by conducting a survey with the purpose of establishing a method for developing human resources for engineers and managers involved in BIM development. In this survey, we analyzed the results of the questionnaire by using "Qualification" as a viewpoint for engineers who carry out development task, and "Ability" as a viewpoint for managers.
Bamboo CNF increases strength and durability of building material. Reducing the numbers of renovation enables to cut Life Cycle Cost, and promote introduction of building material that are effective in reducing CO2. In this paper, the energy saving effect and the thermal environment improvement effect of the CNF building materials by the actual measurement in the actual apartment house and by the simulation.
Air conditioning facilities tend to be designed excessively because of difficulties of estimating peak heat loads. Therefore, their energy efficiencies go down due to increase of low load operation. There is increasing need to use accurate heat load predictions for optimal designs. In this study, heat load prediction model combining dynamic heat load simulation and machine learning was developed and verified using data measured in an existing office building.