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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Data Association Mining for Identifying Cooling Energy Waste Patterns and Corresponding Improving Suggestion
verfasst von : Yucong Xue, Yang Geng, Jiajie Hong, Kang Zhao, Yidong Qian
Erschienen in: Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)
Verlag: Springer Singapore
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Abstract
Due to the great mismatch between the number of occupants and the cooling capacity of air-conditioning system at non-working time in office building, space cooling during non-working time (night cooling) causes enormous waste. In order to identify and reduce cooling energy waste patterns during non-working time, this paper provides a method based on data association mining. As an application for the provided method, an HVAC system, which utilizes river water-source heat pump and water thermal storage technology, in an office building was on-site measured. According to the on-site measure, it could be found that cooling capacity for non-working time is much higher than that was required. Moreover, it was calculated that average COP of river water-source heat pumps rose to 6.7 from 6.3 by eliminating night cooling. The result indicates that reducing the energy used for night cooling is important for energy saving. On the basis, several suggestions for improving energy consumption are proposed for operation.