刘江岩，男，湖南湘潭人。重庆大学能源与动力工程学院，讲师。

2018年获华中科技大学制冷与低温工程专业工学博士学位。长期从事智慧能源相关研究，在能源系统数据挖掘与大数据分析、风险预警与故障辨识、容错控制与节能优化等方面开展了深入和系统的研究工作，目前已发表SCI论文三十余篇（谷歌引用1500余次，H指数24），以第一/通讯作者身份发表SCI\EI论文17篇，国际会议论文十余篇，出版专著1部。

1）新能源汽车智能控制及热安全

2）制冷/热泵/热管理系统故障检测与诊断

3）能源系统大数据分析

4）多能互补分布式能源系统设计与优化

   欢迎对数据挖掘与能源系统交叉研究感兴趣的本科生与我联系交流。

1）制冷系统故障检测与诊断技术

2）基于大数据的新能源汽车动力电池安全监控及智能诊断

3）建筑能源系统数据挖掘及控制优化

4）多能互补分布式能源系统仿真及设计优化

   欢迎对数据挖掘与能源系统交叉研究感兴趣的本科生与我联系交流。

*科研项目：

1）重庆市自然科学基金面上项目“兼顾热、电网损失的西部山地分布式能源系统空间结构与邻里尺度耦合优化研究”，2019.07~2022.06

2）横向课题“冷水机组制冷剂泄漏在线检测技术”，2018~2019

3）横向课题“基于大数据分析及分区多场耦合的空调节能控制技术研究”，2020~2021

4）横向课题“基于强化学习的整车热管理控制”，2021~2022

5）横向课题“热电氧一体化联供装备储能技术研究”,2022~2023

*发表论文：

[1]    Guannan Li, Liang Chen, Jiangyan Liu*, et al. Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis [J]. Energy. 2023, 125943. (JCR一区，IF=8.857)

[2]    Guannan Li, Fan Li, Tanveer Ahmad, Jiangyan Liu*, et al. Performance evaluation of short-term cross-building energy predictions using deep transfer learning strategies[J]. Energy and Buildings. 2022, 250: 112461. (JCR一区，IF=7.201)

[3]    Guannan Li, Fan Li, Tanveer Ahmad, Jiangyan Liu*, et al. Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions[J]. Energy. 2022, 259: 124915. (JCR一区，IF=8.857)

[4]    Jiangyan Liu, Xin Li, Guannan Li, et al. A statistical-based online cross-system fault detection method for building chillers[J]. Building Simulation. 2022, 15(8): 1527-1543. (JCR一区，IF=4.008)

[5]    Jiangyan Liu, Qing Zhang, Xin Li, et al. Transfer learning-based strategies for fault diagnosis in building energy systems[J]. Energy and Buildings. 2021, 250: 111256. (JCR一区，IF=7.201)

[6]    Guannan Li, Yue Zheng, Jiangyan Liu*, et al. An improved stacking ensemble learning-based sensor fault detection method for building energy systems using fault-discrimination information[J]. Journal of Building Engineering. 2021, 43: 102812. (JCR一区，IF=7.144)

[7]    Zhenxiang Dong, Jiangyan Liu*, and Bin Liu et al. 2021. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption patterns classification[J]. Energy and Buildings. 2021: 110929. (JCR一区，IF=7.201)

[8]    Jiangyan Liu*, Qing Zhang, and Zhenxiang Dong et al. 2021. Quantitative evaluation of the building energy performance based on short-term energy predictions[J]. Energy. 2021;223: 120065. (JCR一区，IF=8.857)

[9]    Jiangyan Liu*, Daliang Shi, Guannan Li, et al. Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers[J]. Energy and Buildings. 2020;216:109957. (JCR一区，IF=7.201)

[10] Jiangyan Liu*, Kuining Li, Bin Liu, et al. Improvement of the energy evaluation methodology of individual office building with dynamic energy grading system[J]. Sustainable Cities and Society. 2020;58:102133. (JCR一区，IF=10.696)

[11] Jiangyan Liu, Guannan Li, Bin Liu, et al. Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system[J]. Energy. 2019;174:873-885. (JCR一区，IF=8.857)

[12] Jiangyan Liu, Jiahui Liu, Huanxin Chen, et al. Energy diagnosis of variable refrigerant flow (VRF) systems: Data mining technique and statistical quality control approach[J]. Energy and Buildings, 2018, 175: 148-162. (JCR一区，IF=7.201)

[13] Jiangyan Liu, Huanxin Chen, Jiahui Liu, et al. An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information[J]. Applied Energy, 2017, 206: 193-205.(JCR一区，IF=11.446)

[14] Jiangyan Liu, Jiangyu Wang, Guannan Liet al. Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques[J]. Applied Energy, 2017, 208: 522-539.(JCR一区，IF=11.446)

[15] Jiangyan Liu, Guannan Li, Huanxin Chen, et al. A robust online refrigerant charge fault diagnosis strategy for VRF systems based on virtual sensor technique and PCA-EWMA method[J]. Applied Thermal Engineering, 2017, 119: 233-243.(JCR一区，IF=6.465)

[16] Jiangyan Liu, Yunpeng Hu, Huanxin Chen, et al. A refrigerant charge fault detection method for variable refrigerant flow (VRF) air-conditioning systems[J]. Applied Thermal Engineering, 2016, 107: 284-293.(JCR一区，IF=6.465)

[17] 石大亮，刘江岩*，李夔宁等. 基于关联规则分类的冷水机组故障诊断研究[J]. 制冷学报，2021（01）

[18] 刘江岩，陈焕新，王江宇等. 基于数据挖掘算法的地铁站内温度时序预测方法[J]. 工程热物理学报，2018，38（06）: 1316-1321

[19] 陈焕新，刘江岩，胡云鹏等. 大数据在空调领域的应用[J]. 制冷学报. 2015(04): 16-22.（中国知网高被引论文、高下载论文、高PCSI论文、“第三届中国科协优秀科技论文”）

出版专著：

[1] 陈焕新，刘江岩 等. 制冷空调遇上大数据——行业大变革[M]. 北京: 中国建筑工业出版社，2017