江苏大学主页平台管理系统李康吉--中文主页--论文成果

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李康吉

教授博士生导师硕士生导师
性别：男
毕业院校：浙江大学
学历：博士研究生毕业
学位：博士
在职信息：在职
入职时间： 2002-06-01
办公地点：电气大楼219室
电子邮箱：likangji@ujs.edu.cn

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论文成果

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[1] 李康吉.Fast reconstruction of indoor temperature field for large-space building based on limited sensors: A
[2] 李康吉.An Optimal Control Method for Greenhouse Climate Management Considering Crop Growth’s Spatial Distr
[3] 李康吉.Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network
[4] 李康吉.Data efficient indoor thermal comfort prediction using instance based transfer learning method
[5] 李康吉.An instance based multi-source transfer learning strategy for building's short-term electricity load
[6] 李康吉.Non-Invasive Thermal Comfort Recognition by Monitoring Human Thermal Adaptive Actions and Facial Tem
[7] 李康吉.Building's electricity load prediction using representative data selection and stacking ensemble str
[8] 李康吉.考虑数据分类的建筑电能耗集成预测方法
[9] 李康吉.A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and
[10] 李康吉.Non-invasive human thermal comfort assessment based on multiple angle/distance facial key-region tem
[11] 李康吉.Correlation analysis and modeling of human thermal sensation with multiple physiological markers: An
[12] 李康吉.A non-invasive facial multi-region temperature recognition and comfort prediction
[13] 李康吉.Building?s hourly electrical load prediction based on data clustering and ensemble learning strategy
[14] 李康吉.Non- invasive thermal sensation recognition based on human behavior postures in office environment
[15] 李康吉.Non-invasive thermal sensation recognition based on human behavior postures in office environment
[16] 李康吉.考虑时空变异特性的温室多环境因子优化策略
[17] 李康吉.An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting
[18] 李康吉.Hybrid teaching-learning artificial neural network for city-level electrical load prediction
[19] 李康吉.Short-term electricity consumption prediction for buildings using data-driven swarm intelligence bas
[20] 李康吉.A multiple model approach for predictive control of indoor thermal environment with high resolution

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