Deep Weighted Fusion Learning(DWFL)-based multi-sensor fusion model for accurate building occupancy detection  

作　　者：MdRumman Rafi Fei Hu Shuhui Li Aijun Song Xingli Zhang Zheng O’Neill 

机构地区：[1]Department of Electrical and Computer Engineering,The University of Alabama,Tuscaloosa,AL,USA [2]Mechanical Engineering,Texas A&M University,TX,USA

出　　处：《Energy and AI》2024年第3期542-554,共13页能源与人工智能（英文）

基　　金：supported by the Advanced Research Projects Agency - Energy (ARPA-E), USA under award number DE-AR0001316.

摘　　要：With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition.With the growing demands for power savings,building energy loss reduction could benefit from DL techniques.For buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much necessity.To reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of HVACs.However,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,etc.To overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion structure.DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor.Such weight assignment considers different room conditions and the pros/cons of each type of sensor.To evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)sensors.Among the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

关 键 词：Building energy saving Convolutional neural network(CNN) Occupancy detection Deep learning Multi-sensor fusion Wavelet transform 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]