李康吉 Kangji Li, Ph.D.

Hey, guys! Welcome to the personal web site of Kangji Li!

 

Kangji Li is currently a Professor in the School of Electrical & Information Engineering, Jiangsu University, China. He received his B.Sc. degree (Industrial Automation) and M.Sc. degree (Control Theory and Engineering) from Jiangsu University in 2002 and 2005 respectively. He obtained his Ph.D. degree  (Control Science and Engineering) from the Institute of Cyber-Systems & Control, Zhejiang University, in 2013.

 

 

Mailing Address

School of Electrical & Information Engineering, 

Jiangsu University, Zhenjiang, Jiangsu, 212013, China

Email: likangji@ujs.edu.cn

Phone: 13913433206

 



Research interests

• Distributed parameter system modeling/control

• Model order reduction

• Building energy efficiency

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Research projects

•国家自然科学基金面上项目 (Grant No. 61873114, 2019.01-2022.12)    
"多参数建筑环境的高效率建模、控制与优化方法研究"

•中国博士后面上资助（Grant No. 2016M601741），中国博士后特别资助（Grant No. 2018T110457）

•国家自然科学基金青年项目  (Grant No. 61304075, 2014.01-2016.12)   
"建筑节能中的热环境建模与优化控制问题"

•江苏省自然科学基金青年项目 (Grant No. BK20130538, 2013.07-2016.06) 
"建筑节能中的室内热环境建模与优化控制问题研究"

•江苏大学高级专业人才科研启动基金 (批准号:13JDG112, 2013.09-2016.08)

 

Education experience

1998-2002, School of Electrical & Information Engineering, Jiangsu University, Bachelor

2002-2005, School of Electrical & Information Engineering, Jiangsu University, Master (Advisor: Prof. Guohai Liu) 

2009-2013, Institute of Cyber-Systems and Control, Zhejiang University, PhD (Advisor: Prof. Jian Chu and Prof. Hongye Su) Dissertation Title: Modeling, Control and Optimization for indoor 

Environment and Energy Consumption Prediction

Research experience

2003-2007 Teaching Assistant, Jiangsu University

2007-2014 Lecturer, Jiangsu University

2014-2020 Associate Professor, Jiangsu University

2020-          Professor, Jiangsu University

 

Selected Journal Publications (full pubilication list at Google Scholar Citation page)

[19]. K. Li, J. Tian, W. Xue* and G. Tan*. Short-term electricity consumption prediction for buildings using data-driven swarm intelligence based ensemble model. Energy and Buildings, Vol.231, 110558, 2021.

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[18]. J. Tian, K. Li* and W. Xue. An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting. Sustainable Cities and Society, Vol.66, 102654, 2021.

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[17]. K. Li*, Z. Sha, W. Xue, X. Chen, H. Mao and G. Tan. A fast modeling and optimization scheme for greenhouse environmental system using proper orthogonal decomposition and multi-objective genetic algorithm. Computers and Electronics in Agriculture, Vol.168, 105096, 2020.

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[16]. K. Li*, X. Xie, W. Xue and X. Chen. Hybrid teaching-learning artificial neural network for city-level electrical load prediction. Science China-Information Sciences, Vol.63(5), 159204,  2020.

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[15]. K. Li, W. Xue*, G. Tan and AS. Denzer. A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms. Building Services Engineering Research & Technology, Vol.41(1), pp.108-127,2020.

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[14]. W. Xue, C. Wang, J. Tian and K. Li*. Hybrid wind power forecasting based on extreme learning machine and improved TLBO algorithm. Journal of Renewable and Sustainable Energy, Vol.12, 053309, 2020.

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[13]. X. Chen, K. Li, B. Xu, W. Xue and ZL. Yang. Biogeography-based learning particle swarm optimization for combined heat and power economic dispatch problem. Knowledge-based Systems, Vol. 208,106463, 2020.

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[12]. K. Li*, W. Xue, H. Mao, X. Chen, H. Jiang and G. Tan. Optimizing the 3D Distributed Climate inside Greenhouses Using Multi-Objective Optimization Algorithms and Computer Fluid Dynamics. Vol. 12(15), 2873, 2019.

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[11]. K. Li, X. Xie, W. Xue, X. Chen and Y. Yang. A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction. ​Energy and Buildings, Vol. 174, pp. 323-334, 2018. 

 

[10]. K. Li*, W. Xue, C. Xu and H. Mao. A multiple model approach for predictive control of indoor thermal environment with high resolution. Journal of Building Performance Simulation, Vol. 11(2), pp. 164-178, 2018. 

 

[9]. K. Li, L. Pan, W. Xue, H. Jiang and H. Mao. Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study. Energies. Vol. 10(2), pp. 245:1-23, 2017. 

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[8].K. Li, W. Xue and G. Liu. Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method. Energies. Vol. 10(2), pp. 231:1-15, 2017. 

 

[7].K. Li,  C.Hu, G. Liu and W. Xue. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. ​Energy and Buildings, Vol. 108, pp. 106-113, 2015. 

 

[6].W. Xue, K. Li* and G.Liu. DDI-based finite-time stability analysis for nonlinear switched systems with time-varying delays. International Journal of Systems Science, Vol.47(12), pp. 3027-3035, 2016. 

 

[5].W. Xue and K. Li*. Positive finite-time stabilization for discrete-time linear systems. Journal of Dynamic Systems, Measurement, and Control, Vol. 137(1): 014502, 2015. 



[4].K. Li, W. Xue, C. Xu and H. Su. Optimization of ventilation system operation in office environment using POD model reduction and genetic algorithm. Energy and Buildings, Vol.67, pp. 34-43, 2013. 



[3].K. Li, H. Su, J. Chu and C. Xu. A fast-POD model for simulation and control of indoor thermal environment of buildings. Building and Environment, Vol. 60, pp. 150-157, 2013. 



[2].K. Li, H. Su and J. Chu. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Buildings, Vol. 43(10), pp. 2893-2899, 2011.

 

[1].K. Li and H. Su. Forecasting building energy consumption with hybrid genetic algorithm hierarchical adaptive network-based fuzzy inference system. Energy and Buildings, Vol. 42(11), pp. 2070-2076, 2010.

 

Proceeding/Conference Publications

[9].W. Zhou, K. Li*, W. Xue, H. Jiang and H. Mao. Multi-objective Optimization of Fan-Pad System Operation for Venlo Greenhouse Using CFD Model based Data Interactive Mechanism. The 36th

Chinese Control Conference, Jul, 2017, Dalian, Liaoning, China. (EI)

 

[8].L. Pan, K. Li*, W. Xue and G. Liu. Multi-objective Optimization for Building Performance Design Considering Thermal Comfort and Energy Consumption. The 35th Chinese Control Conference, Jul, 2016, Chengdu, Sichuan, China. (EI)

 

[7].K. Li, W. Xue and G. Liu. An optimization framework for ventilation system operation in office environment using data interactive mechanism. CLIMA 2016, May, 2016, Aalborg, Denmark. (EI)

 

[6].K. Li, W. Xue and G. Liu. A general optimization framework for complex PDE models based on data interactive mechanism . IEEE International Conference on Advanced Intelligent Mechatronics, JUL, 2015, Busan, Korea.

 

[5].C. Hu, K.Li*, G.Liu and L.Pan.  Forecasting building energy consumption based on hybrid PSO-ANN prediction model. The 34st Chinese Control Conference, Jul, 2015, Hangzhou, Zhejiang, China.

 

[4].W. Xue and K. Li*.  Input-output finite-time stability of time-delay systems and its application to 

active vibration control. IEEE International Conference on Automation Science and Engineering, Aug, 2014, Taipei, Taiwan.

 

[3].K. Li, H. Su and J. Chu. Optimal control strategy of ventilation systems using POD model reduction and genetic algorithm. CLIMA2013, Jun, 2013, Prague, Czekh.



[2].K. Li, H. Su and J. Chu. Nonlinear model reduction for simulation and control of temperature distribution in air conditioned rooms. The 31st Chinese Control Conference, Jul, 2012, Hefei, Anhui, China.



[1].K. Li, H. Su and J. Chu. A CFD-based test method for control of indoor environment in air conditioned rooms. The 31st Chinese Control Conference, Jul, 2012, Hefei, Anhui, China.

 

Book Publications

[1].李康吉. 建筑室内环境建模、控制与优化及能耗预测. 德国：金琅出版社，2016.

 

[2].仇保兴（注: 住建部副部长）. 建筑节能与绿色建筑模型系统导论. 北京: 中国建筑工业出版社, 2011.（李康吉编著2.3, 3.3~3.7, 4.2, 4.5章节）

[3].刘国海, 李康吉, 薛文平. 现场总线Profinet. 北京: 电子工业出版社, 2007.

Patents

[1].薛文平,李康吉,刘国海. 一种针对冲击型路面扰动的汽车悬架系统主动控制方法. 申请号：201510076570.3 （发明专利）

 

[2]. 李康吉,薛文平,刘国海. 用于改善建筑物内环境的交互式优化方法. 申请号：201510076570.3 （发明专利）

 

[3]. 胡程磊,李康吉,薛文平等. 一种基于神经网络和微粒群优化算法的建筑能耗预测方法. 申请号：201410709145.9 （发明专利）

 

[4].李康吉, 薛文平. 一种基于模型降阶技术的建筑室内环境优化方法. 授权公告号: CN103049612B.（发明专利）

 

[5].薛文平, 李康吉. 一种基于模型降阶和多模型预测控制的室内热环境控制方法. 申请公布号：

CN103995548A （发明专利）

 

[6]. 李康吉, 薛文平. 建筑能耗预测软件v1.0. 软件著作权号：2015SR012235.

 

[7]. 潘磊, 李康吉. 基于PSO-BP算法的建筑电能耗预测软件v1.0. 软件著作权号：2015SR051766.

 

Awards

[1]. 2013年镇江市优秀科技论文三等奖.

(A fast-POD model for simulation and control of indoor thermal environment of buildings)

 

[2]. 2014年入选江苏大学 “青年骨干教师培养工程”.

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[3]. 2018年 Elsevier 杰出审稿人 Outstanding Contribution in Reviewing

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update date: 2021-02