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Yixuan Wei
Researcher at The University of Nottingham Ningbo China
Publications - 11
Citations - 866
Yixuan Wei is an academic researcher from The University of Nottingham Ningbo China. The author has contributed to research in topics: Energy consumption & Energy engineering. The author has an hindex of 8, co-authored 11 publications receiving 514 citations. Previous affiliations of Yixuan Wei include University of Science and Technology Beijing.
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A review of data-driven approaches for prediction and classification of building energy consumption
TL;DR: The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies and paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands.
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A review of thermal absorbers and their integration methods for the combined solar photovoltaic/thermal (PV/T) modules
Jinshun Wu,Xingxing Zhang,Xingxing Zhang,Jingchun Shen,Yupeng Wu,Karen Connelly,Tong Yang,Llewellyn Tang,Manxuan Xiao,Yixuan Wei,Ke Jiang,Chao Chen,Peng Xu,Hong Wang +13 more
TL;DR: In this article, the authors conduct a critical review on the essential thermal absorbers and their integration methods for the currently available PV modules for the purpose of producing the combined PV/T modules.
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Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks
Yixuan Wei,Liang Xia,Song Pan,Song Pan,Jinshun Wu,Xingxing Zhang,Mengjie Han,Zhang Weiya,Jingchao Xie,Li Qingping +9 more
TL;DR: The result shows that the occupancy number, as the input, is able to improve the accuracy in predicting energy consumption using a neural-network model.
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A model based on Gauss Distribution for predicting window behavior in building
TL;DR: A novel modeling method is provided that can be used to predict window states more accurately in office buildings, and shows that Gauss distribution models could provide higher prediction accuracy, with 9.5% higher than Logistic regression model when using suitable inputs.
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Analysis and interpretation of the particulate matter (PM10 and PM2.5) concentrations at the subway stations in Beijing, China
TL;DR: Wang et al. as mentioned in this paper analyzed and interpreted the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China.