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.
A method to optimize natural ventilation operation at a building design stage is proposed. The method considers an aspect that frequent open and close of natural ventilation windows may cause occupant's complaints and result in the barrier for the optimal usage to minimize building energy consumption. The operation history evaluation utilizing a network simulation at building design stage could solve the problem. This paper presents a case study to carry out the method. The optimal operation strategies based on the operation history are proposed considering building user and operator points of view.
The object of this study is to develop a new assessment method for indoor air quality using biosensor system which can evaluate airborne hazards directly because it uses the behavior of organisms as an indicator of air quality. In this study, Medaka, a type of Japanese killifish, was chosen as the biosensor media. Recorded by CCD cameras, the 3D swimming motions of the Medaka in water containing chemical substances are quantified. When exposed to water containing chemical substances, the Medaka shows several types of abnormal behavior. Accordingly, the correlation between the degree of chemical pollution in the water and such behavior by the fish is investigated. Based on this system, the level of chemical substance (i.e. pollution) in the air can be predicted by the movements of the Medaka.
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