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

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-05-24 DOI:10.1016/j.egyai.2024.100379
Md. Rumman Rafi , Fei Hu , Shuhui Li , Aijun Song , Xingli Zhang , Zheng O’Neill
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Abstract

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 (CO2), 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%.

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基于深度加权融合学习(DWFL)的多传感器融合模型,用于准确检测建筑物占用情况
近年来,随着人工智能的发展,深度学习(DL)模型因其无需人工预先定义特征即可处理复杂模式识别的能力,在普通机器学习(ML)算法中占据了主导地位。随着节电需求的不断增长,减少建筑能耗可从 DL 技术中获益。对于居住人数不等的楼宇/房间来说,供暖、通风和空调系统(HVAC)在运行时往往没有太多必要。为了减少建筑物的能源损耗,可以利用精确的占用检测/预测(ODP)结果来控制暖通空调系统的正常运行。然而,由于传感器选择/部署不当、模式识别学习算法效率低下、房间条件多变等多种原因,占用检测是一个具有挑战性的问题。为了克服上述挑战,我们提出了一种基于 DL 的框架,即深度加权融合学习(DWFL),以最优的多传感器融合结构来检测和预测占用率。DWFL 将从多种类型传感器中提取的特征与每个传感器的优先级/权重分配相融合。这种权重分配考虑了不同的房间条件和每种传感器的优缺点。为了评估 DWFL 模型的占用预测准确性,我们利用低成本摄像头、二氧化碳(CO2)传感器和被动红外(PIR)传感器建立了一个实验测试平台。在最近提出的占用检测模型中,DeepFusion 在异构传感器数据上使用了深度学习模型,在占用计数估计方面达到了 88% 的准确率(Xue 等人,2019 年)。另一个基于深度学习的模型 MI-PIR 在 PIR 传感器的原始模拟数据上取得了 91% 的准确率(Andrews 等人,2020 年)。我们的研究成果是 94%。因此,实验结果表明,我们的 DWFL 方案比最先进的 ODP 方法高出 3%。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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