| Year |
Citation |
Score |
| 2020 |
Zhang C, Li J, Zhao Y, Li T, Chen Q, Zhang X. A hybrid deep learning-based method for short-term building energy load prediction combined with an interpretation process Energy and Buildings. 225: 110301. DOI: 10.1016/J.Enbuild.2020.110301 |
0.317 |
|
| 2019 |
Zhao Y, Li T, Zhang X, Zhang C. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future Renewable & Sustainable Energy Reviews. 109: 85-101. DOI: 10.1016/J.Rser.2019.04.021 |
0.321 |
|
| 2019 |
Zhao Y, Li T, Fan C, Lu J, Zhang X, Zhang C, Chen S. A proactive fault detection and diagnosis method for variable-air-volume terminals in building air conditioning systems Energy and Buildings. 183: 527-537. DOI: 10.1016/J.Enbuild.2018.11.021 |
0.31 |
|
| 2019 |
Zhang C, Zhao Y, Zhang X. An improved association rule mining-based method for discovering abnormal operation patterns of HVAC systems Energy Procedia. 158: 2701-2706. DOI: 10.1016/J.Egypro.2019.02.025 |
0.3 |
|
| 2019 |
Zhang C, Xue X, Zhao Y, Zhang X, Li T. An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems Applied Energy. 253: 113492. DOI: 10.1016/J.Apenergy.2019.113492 |
0.309 |
|
| 2019 |
Fan C, Sun Y, Zhao Y, Song M, Wang J. Deep learning-based feature engineering methods for improved building energy prediction Applied Energy. 240: 35-45. DOI: 10.1016/J.Apenergy.2019.02.052 |
0.305 |
|
| 2018 |
Zhao Y, Shrivastava AK, Tsui KL. Regularized Gaussian Mixture Model for High-Dimensional Clustering. Ieee Transactions On Cybernetics. PMID 29994696 DOI: 10.1109/Tcyb.2018.2846404 |
0.536 |
|
| 2018 |
Fan C, Xiao F, Zhao Y, Wang J. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data Applied Energy. 211: 1123-1135. DOI: 10.1016/J.Apenergy.2017.12.005 |
0.317 |
|
| 2017 |
Fan C, Xiao F, Zhao Y. A short-term building cooling load prediction method using deep learning algorithms Applied Energy. 195: 222-233. DOI: 10.1016/J.Apenergy.2017.03.064 |
0.32 |
|
| 2016 |
Zhao Y, Shrivastava AK, Tsui KL. Imbalanced classification by learning hidden data structure Iie Transactions (Institute of Industrial Engineers). 48: 614-628. DOI: 10.1080/0740817X.2015.1110269 |
0.53 |
|
| 2014 |
Zhao Y, Xiao F, Wen J, Lu Y, Wang S. A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers Hvac&R Research. 20: 798-809. DOI: 10.1080/10789669.2014.938006 |
0.314 |
|
| 2013 |
Zhao Y, Wang S, Xiao F. A system-level incipient fault-detection method for HVAC systems Hvac&R Research. 19: 593-601. DOI: 10.1080/10789669.2013.789371 |
0.311 |
|
| 2013 |
Zhao Y, Wang S, Xiao F, Ma Z. A simplified physical model-based fault detection and diagnosis strategy and its customized tool for centrifugal chillers Hvac&R Research. 19: 283-294. DOI: 10.1080/10789669.2013.765299 |
0.3 |
|
| 2013 |
Zhao Y, Wang S, Xiao F. A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression Applied Thermal Engineering. 51: 560-572. DOI: 10.1016/J.Applthermaleng.2012.09.030 |
0.315 |
|
| 2013 |
Zhao Y, Wang S, Xiao F. Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD) Applied Energy. 112: 1041-1048. DOI: 10.1016/J.Apenergy.2012.12.043 |
0.313 |
|
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