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Overlapping of cooking behavior in high-rise residential buildings
Abstract The design of a centralized cooking-fume exhaust system for high-rise residential buildings lacks the parameters related to cooking behavior, such as the coincidence factors. This study surveyed 12-, 14-, and 25-floor residential units by using a thermometer to obtain the continuous temperature profiles for study cooking behavior. Python was used to identify the breaks of temperature gradient, and continuous temperature profiles were transformed into a group of step signals consisting of only “0” and “1” while interfering fluctuations were eliminated and turning points were determined. The hourly overlapping rate of the residential unit after data processing is highly similar. The abstraction of cooking behavior is a mathematical statistical model that obeys normal distribution. A typical cooking behavior model based on normal distribution was established using the main cooking periods, kurtosis, and skewness values, the modeling step length, the peak value of the coincidence factors, and the time corresponding to the peak. Using different correction coefficients to correct the peak value of the coincidence factors can extend the application scope of the model to buildings of any height. In addition, the coincidence factor of 50% can be used as a design parameter for the flue cross section and fan selection parameters. Thus, the selected fan, which is fully loaded during cooking periods and has low load during non-cooking periods, has significant energy saving potential.
Overlapping of cooking behavior in high-rise residential buildings
Abstract The design of a centralized cooking-fume exhaust system for high-rise residential buildings lacks the parameters related to cooking behavior, such as the coincidence factors. This study surveyed 12-, 14-, and 25-floor residential units by using a thermometer to obtain the continuous temperature profiles for study cooking behavior. Python was used to identify the breaks of temperature gradient, and continuous temperature profiles were transformed into a group of step signals consisting of only “0” and “1” while interfering fluctuations were eliminated and turning points were determined. The hourly overlapping rate of the residential unit after data processing is highly similar. The abstraction of cooking behavior is a mathematical statistical model that obeys normal distribution. A typical cooking behavior model based on normal distribution was established using the main cooking periods, kurtosis, and skewness values, the modeling step length, the peak value of the coincidence factors, and the time corresponding to the peak. Using different correction coefficients to correct the peak value of the coincidence factors can extend the application scope of the model to buildings of any height. In addition, the coincidence factor of 50% can be used as a design parameter for the flue cross section and fan selection parameters. Thus, the selected fan, which is fully loaded during cooking periods and has low load during non-cooking periods, has significant energy saving potential.
Overlapping of cooking behavior in high-rise residential buildings
Wang, Yirui (author) / Gao, Jun (author) / Zeng, Lingjie (author) / Tong, Leqi (author) / Liu, Guodong (author) / Cao, Changsheng (author)
Energy and Buildings ; 210
2020-01-06
Article (Journal)
Electronic Resource
English
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