Journal ArticleDOI
Development of deep learning-based equipment heat load detection for energy demand estimation and investigation of the impact of illumination
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This article is published in International Journal of Energy Research.The article was published on 2021-04-01. It has received 9 citations till now. The article focuses on the topics: Deep learning & HVAC.read more
Citations
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Journal ArticleDOI
A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand
TL;DR: Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.
Journal Article
Energy saving estimation for plug and lighting load using occupancy analysis
Prashant Anand,David Cheong,Chandra Sekhar,Mattheos Santamouris,Mattheos Santamouris,Sekhar Kondepudi +5 more
TL;DR: In this paper, the authors investigated the relationship of occupancy with plug and lighting loads energy consumption for several spaces of an institutional building floor and developed a model for K as a function of occupancy using multiple non-linear regression (MNLR) and deep neural network (DNN) based algorithms.
Journal ArticleDOI
Indoor fire detection utilizing computer vision-based strategies
TL;DR: In this article , a vision-based indoor fire and smoke detection system was proposed for both indoor and outdoor environments, which is based on the Faster R-CNN Inception V2 and SSD MobileNet V2 models.
Journal ArticleDOI
Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand
TL;DR: In this article , a region-based Convolutional Neural Network (R-CNN) was used for real-time detection and recognition of window operations in buildings, achieving an accuracy of 85.63% for occupancy activity detection and 92.20% for window operation detection.
Journal ArticleDOI
Vision-based human activity recognition for reducing building energy demand:
TL;DR: The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments and reduce the over- or under-estimation of occupancy heat gains.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.