The VRF system frequently has refrigerant charge amount (RCA) fault, and this causes a large amount of building energy waste. The research on data-driven models used to diagnose this fault mostly focuses on the optimization of a single model, and it is difficult to maintain good performance at different fault levels. In view of this, this paper proposes the RCA fault diagnosis strategy of VRF system based on Stacking ensemble learning. Firstly, the strategy selects the low dimensional feature set through Recursive Feature Elimination (RFE) and correlation analysis. Then the initial Stacking ensemble learning model consists of two levels of learners. The output of the first-level learners and the original feature set are used as the feature input of the second-level learners. Then, the composition of the first-level learners in the model is adjusted according to the feature importance ranking re-sults of the RFE method. The results show that the classification accuracy (CA) of the model optimized by the proposed strategy in the training set and the test set is improved by 3.9% and 4.02% respectively, and there is little difference between the two, indicating the model generalization ability is improved.
机构:
Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Asad, Hussain Syed
Wan, Hang
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City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R ChinaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Wan, Hang
Kasun, Hewage
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Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Kasun, Hewage
Rehan, Sadiq
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Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Rehan, Sadiq
Huang, Gongsheng
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City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R ChinaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
机构:
Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Asad, Hussain Syed
Wan, Hang
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R ChinaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Wan, Hang
Kasun, Hewage
论文数: 0引用数: 0
h-index: 0
机构:
Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Kasun, Hewage
Rehan, Sadiq
论文数: 0引用数: 0
h-index: 0
机构:
Univ British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, CanadaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada
Rehan, Sadiq
Huang, Gongsheng
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R ChinaUniv British Columbia, Life Cycle Management Lab LCML, Sch Engn, Vancouver, BC, Canada