PROFESSIONAL EVENT KEYNOTE-SPEAKER
Data-Driven Thermal Comfort Modeling for Smart Buildings
We perform data analysis using the IoT generat- ed building data to derive accurate thermal com- fort model for smart building control. Deep neu- ral network (DNN) is used to model the rela- tionship between the controllable building oper- ations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fi- ne-grained architecture is proposed, where an exclusive model is trained for each factor and accordingly the corresponding thermal comfort can be evaluated. The experimental results show that the proposed fine-grained DNN outper- forms its coarse-grained counterpart by 3.5× and is 1.7×, 2.5×, 2.4×, and 1.9× more accurate compared to four popular machine learning al- gorithms.
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