Transfer Learning Approach for Occupancy Prediction in Smart Buildings
Transfer Learning Approach for Occupancy Prediction in Smart Buildings
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DOI：
10.1109/irec51415.2021.9427869
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Mohamad Khalil;S. McGough;Z. Pourmirza;Mehdi Pazhoohesh;S. Walker
中科院分区：
文献类型：
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作者：
Mohamad Khalil;S. McGough;Z. Pourmirza;Mehdi Pazhoohesh;S. Walker
智能建筑中准确的占用预测是降低建筑能耗和有效控制HVAC系统（暖通空调）的关键因素，从而增加人类舒适度。这项工作的重点是在智能建筑中使用环境传感器数据的占用预测建模（占用/未占用）的问题。当历史训练数据量有限时，使用一种新的迁移学习方法来提高占用预测的准确性。将所提出的方法和模型应用于教育大楼三个办公室的案例研究。本研究中使用的数据集是从纽卡斯尔大学城市科学大楼（USB）收集的实际数据。所提出的迁移学习方法的结果进行了比较，从支持向量机和随机森林算法的模型。最后的结果表明，在这项研究中，最准确的模型来预测占用状态是由堆叠的长短期记忆迁移学习框架。
Accurate occupancy prediction in smart buildings is a key element to reduce building energy consumption and control HVAC systems (Heating – Ventilation and– Air Conditioning) efficiently, resulting in an increment of human comfort. This work focuses on the problem of occupancy prediction modelling (occupied / unoccupied) in smart buildings using environmental sensor data. A novel transfer learning approach was used to enhance occupancy prediction accuracy when the amounts of historical training data are limited. The proposed approach and models are applied to a case study of three office rooms in an educational building. The data sets used in this work are actual data collected from the Urban Sciences Building (USB) in Newcastle University. The results of the proposed transfer learning approach have been compared with the models from Support Vector Machine and Random Forest algorithms. The final results demonstrate that the most accurate model in this study to predict occupancy status was produced by stacked Long-Short-Term-Memory with a transfer learning framework.