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• W2743559203 abstract "The different aspects of building energy system operations can be analyzed from historic data that are captured by various sensors during building operation. The analysis of these different features helps to understand the operational behavior of the building's energy systems, which can contribute to plan efficient use of energy in buildings. One of the major information that can help in proper planning of energy consumption is to have a method that can automatically detect the occupancy in buildings with the different sensor data used for monitoring. The data of an air ventilation system, which is responsible for maintaining appropriate indoor air quality, is used for this research. This paper presents a method that uses the K-means clustering algorithm for automatic detection of occupants using air quality data of the ventilation system. The proposed method has been applied on real monitoring data and results are discussed in detail, showing a good identification of the detected occupancy with the advantage of saving the costs for additional sensor equipment." @default.
• W2743559203 created "2017-08-17" @default.
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• W2743559203 date "2017-06-01" @default.
• W2743559203 modified "2023-10-14" @default.
• W2743559203 title "Automatic occupancy prediction using unsupervised learning in buildings data" @default.
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• W2743559203 doi "https://doi.org/10.1109/isie.2017.8001463" @default.
• W2743559203 hasPublicationYear "2017" @default.
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