Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 2, Pages 2540-2549Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2018.2871461
Keywords
Deep learning; smart building; smart city; thermal comfort
Categories
Funding
- Singapore National Research Foundation (NRF) via the Green Buildings Innovation Cluster (GBIC) [NRF2015ENC-GBICRD001-012]
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The emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fine-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 outperforms its coarse-grained counterpart by 3.5x and is 1.7x, 2.5x, 2.4x, and 1.9x more accurate compared to four popular machine learning algorithms. Besides, DNN's performance promotes with deeper network topology and more neurons, and a simple topology with the same number of neurons per network hidden layer is sufficient to achieve high modeling accuracy. Finally, the derived thermal comfort model reveals a linear relationship between comfort and air conditioning setpoint. The linear property helps quickly and accurately search for the optimal controllable setpoint with the desired comfort.
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