The Criteria of Outdoor Conditions for Operating Natural Ventilation Openings

Yamamoto, Y.; Tanabe, Shin‐ichi

doi:10.3130/aije.81.375

Cited by 13 publications

(10 citation statements)

References 4 publications

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“…In this paper, two types of map are shown with two main control logics for Japan 22 . The first logic, t‐h type , constrains the upper and lower limits of the outdoor air temperature and the upper limit of the outdoor air enthalpy, as shown in Figure 1A.…”

Section: Methodsmentioning

confidence: 99%

“…Each limit value is set as the representative value in Japanese office buildings. 22 Each value is shown in Figure 1. The total usable hours are defined as the annual sum of hours when the outdoor conditions are within the operational range during office hours, 9:00-19:00.…”

Section: Total Natural Ventilation Operation Hoursmentioning

confidence: 99%

“…In this paper, two types of map are shown with two main control logics for Japan. 22 The first logic, t-h type, constrains the upper and lower limits of the outdoor air temperature and the upper limit of the outdoor air enthalpy, as shown in Fig- ure 1A. The second type, the t-x type, constrains the upper and lower limits of both the outdoor air temperature and the dew point temperature, as shown in Figure 1B.…”

Section: Total Natural Ventilation Operation Hoursmentioning

confidence: 99%

“…The original settings for the upper and lower limits are 26°C and 15°C, respectively. Although each changed value is set by plus or minus 2°C, those can be regarded as realistic ones comparing to actual settings reported in the Ref [22]. [Colour figure can be viewed at wileyonlinelib rary.com] Sensitivity of the upper and lower limits for outdoor air temperature to the total natural ventilation hours when the t-x type is applied.…”

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confidence: 99%

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Regional classification maps for engineered natural ventilation design of office buildings in Japan

Hiyama

2020

Japan Architectural Review

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Natural ventilation is an essential component of green building design. Thoughtful consideration of natural ventilation in the early stages of building design is important for its successful integration. In this paper, we suggest the utilization of regional classification maps. Two maps are presented: a map of target air change rates for natural ventilation and a map of the total hours in which natural ventilation can be used throughout the year. The target air change rate is defined as the point at which the slope of the increase in the cooling effect due to natural ventilation reaches a maximum, and the proposed maps show the distributions of the target air change rate in Japan. The total natural ventilation hours are determined by calculating the period during which the outdoor air is usable for natural ventilation. The maps were created based on 836 points of weather data from Japan. These maps can help architects and building engineers understand the effects expected from natural ventilation system integration during the process of decision making in the early stages of building design.

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Section: Methodsmentioning

confidence: 99%

Section: Total Natural Ventilation Operation Hoursmentioning

confidence: 99%

Section: Total Natural Ventilation Operation Hoursmentioning

confidence: 99%

mentioning

confidence: 99%

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Regional classification maps for engineered natural ventilation design of office buildings in Japan

Hiyama

2020

Japan Architectural Review

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“…Buildings without ventilation facilities rely on natural ventilation. In general, natural ventilation is widely used in Japan when the outdoor temperature is 15-25 • C [29]. However, to reduce the infection rate of COVID-19 indoors, it is necessary to secure a large amount of ventilation regardless of the season or outdoor temperature.…”

Section: Introductionmentioning

confidence: 99%

Studying the Indoor Environment and Comfort of a University Laboratory: Air-Conditioning Operation and Natural Ventilation Used as a Countermeasure against COVID-19

Kuwahara

Kim

2022

Buildings

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In this study, the indoor air quality and thermal environment of a university facility were analyzed when an air conditioner was operated and natural ventilation was provided; the most effective natural ventilation method was also evaluated. The research conditions were established by adjusting the temperature of the air conditioner, and frequency of window openings every hour. The area around the windows that is open for natural ventilation was easily affected by outdoor air temperature and humidity. However, since the air conditioner was operating, there was only a brief period during which the environment was uncomfortable. Therefore, the participants in the questionnaire survey expressed neutrality or slight satisfaction for the thermal environment of the entire space. Setting the room temperature to 25 °C in summer was highly comfortable and generated a satisfactory indoor thermal environment. When the room temperature was set to 20 °C in winter, the thermal comfort level was higher than in the other conditions. Providing natural ventilation for 5 min every 30 min was determined to be effective in maintaining an indoor CO2 concentration of 1000 ppm or less. Facilitating natural ventilation for 10 min every 60 min allowed the entry of a large amount of fresh air; however, due to the extended period in which the windows and doors were closed, there were instances when the indoor CO2 concentration exceeded 1000 ppm. Therefore, providing frequent natural ventilation with short time intervals is effective for improving indoor air quality.

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Operation strategy for engineered natural ventilation using machine learning under sparse data conditions

Hiyama

Takeuchi

Omodaka

et al. 2021

Japan Architectural Review

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Machine learning (ML) is a useful technique for improving building operations. However, if data can only be obtained from a target building, the data shortage will limit the use of general ML models. To overcome this issue, simplified targets and limited numbers of feature variables are required. In building engineering, building physics can be used to promote ML implementations. In this paper, a case study targeting the operation of engineered natural ventilation is performed based on energy simulations. The target issue is simplified to select the preferable opening pattern, full or half open, throughout the day. Two models using two or three feature variable types are compared. The ML method can effectively predict the correct operation scheme, even in the first year of use. The correct answer rate in the case of three variable types increases from 83% to 95% in the second year, although no significant improvement is observed in the case with two variable types. The results imply the strategy of creating a simplified model first and improving the model following data acquisition works for ML model implementation in building operations.

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The Criteria of Outdoor Conditions for Operating Natural Ventilation Openings

Cited by 13 publications

References 4 publications

Regional classification maps for engineered natural ventilation design of office buildings in Japan

Regional classification maps for engineered natural ventilation design of office buildings in Japan

Studying the Indoor Environment and Comfort of a University Laboratory: Air-Conditioning Operation and Natural Ventilation Used as a Countermeasure against COVID-19

Operation strategy for engineered natural ventilation using machine learning under sparse data conditions

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