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Deep Learning Occupancy Activity Detection Approach for Optimising Building Energy Loads

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Deep Learning Occupancy Activity Detection Approach for Optimising Building Energy Loads International Conference on Applied Energy 2020 Dec. 1 - Dec. 10, 2020, Bangkok / Virtual Paper ID: 127 Deep learning occupancy activity detection approach for optimising building energy loads Paige Wenbin Tien*, Shuangyu Wei, John Kaiser Calautit, Jo Darkwa, Christopher Wood Department of Architecture and Built Environment, University of Nottingham, Nottingham, United Kingdom *e-mail: [email protected], [email protected] ABSTRACT Heating, ventilation and air- HVAC The main aim of this paper is to develop a vision- conditioning based deep learning method for real-time occupancy activity detection and recognition to help the operations 1. INTRODUCTION AND LITERATURE REVIEW of building energy systems. A faster region-based The built environment sector is responsible for up to convolutional neural network was developed, trained 35% of the global energy use and energy-related and deployed to an artificial intelligence (AI)-powered emissions [1]. Reducing the energy consumption of camera for the application of real-time occupancy buildings requires innovative methods. Solutions such as activity detection. Initial experimental tests were occupancy-based controls can achieve significant energy performed within an office space of a selected case study savings by eliminating unnecessary energy usage. building. The detection provided correct detections for A significant element affecting the usage of these the majority of the time (97.32%). Average detection energy consumers is the behaviour of the occupants [2]. accuracy of 92.20% was achieved for all activities. For instance, rooms in offices or lecture theatres are not Building energy simulation of the case study building was fully utilised or occupied during the day. While the HVAC performed to assess the potential energy savings that systems which operates based on a fixed set point can be achieved. The impact of using the typical schedules assumes maximum occupancy. The use of schedules and deep learning influenced profiles (DLIP) fixed set points in combination with varying occupancy were assessed. The work has shown that the generation patterns could lead to rooms frequently being over- or of the real-time DLIP from the ability to enable prediction under-conditioned. Peng et al. [3] have shown that those and generation of real-time occupancy activity-based average daily occupancy rates were rarely over 60%, in internal heat gains data can inform building energy particular in single-person offices. While equipment or management systems and controls of the heating, appliances in offices can be kept in operations during the ventilation and air-conditioning (HVAC) for a more entire working day, irrespective of the patterns of accurate and optimized operation. occupancy [4]. This indicates the need for the development of solutions such as demand-driven Keywords: Artificial intelligence, energy management, controls that adapts to occupancy patterns in real-time occupancy detection, activity detection, HVAC system, and optimise HVAC operations while providing deep learning. comfortable conditions. Shih [5] have shown that the integration of occupancy information into the HVAC NONMENCLATURE operation can lead to significant energy savings. Abbreviations Information on real-time occupancy patterns is central to the effective development and AI Artificial intelligence implementation of a demand-driven control strategy for BES Building energy simulation HVAC [6]. Several sensors and technologies [7] can be CNN Convolutional neural network used to measure and monitor real-time occupancy. This DLIP Deep learning influenced profile includes motion sensors, environmental sensors, Selection and peer-review under responsibility of the scientific committee of the 12th Int. Conf. on Applied Energy (ICAE2020). Copyright © 2020 ICAE wearables and Wi-Fi sensors. These have shown the management system and controls of the building HVAC capabilities of strategies in sensing occupancy system to make adjustments based on the actual information through the count and location of occupants building conditions while minimising unnecessary loads. in spaces and aid demand-driven control systems. However, for initial analysis, the DLIP profiles were However, there is limited research on sensing the actual inputted into building energy simulation to identify activities performed by occupants which can affect the potential reductions in building energy consumption and indoor environment conditions. The activities of changes within the indoor environment. occupants can affect the internal heat gains (sensible and latent heat) in spaces directly [8] and indirectly [9]. This suggests the need for a solution to enable accurate understanding of occupancy activity levels to provide greater understanding of the heat emitted by occupants for better estimation of the building heating and cooling requirements. A potential solution is to use artificial intelligence (AI) based techniques. This includes a computer vision based approach based on deep learning techniques to detect and recognise the activities of occupants [10]. Several works [11, 12] have been developed using on Fig 1 An overview of the proposed framework approach and method vision-based deep learning methods to identify human activities. Studies attempted to improve the 2.1 Deep Learning Method performance of understanding human presence within a To enable the application of occupancy activity building space rather than achieving data to understand detection, the general process to establish a occupancy actions to seek solutions towards the convolutional neural network (CNN) based deep learning minimisation of unnecessary building energy loads. detection model was followed. Images of various types Additionally, no work has attempted to predict the of occupancy activities were gathered and processed to associated occupancy heat emission of both sensible and form the input datasets. Images related to the activities latent gains. Limited studies performed tests of the of napping, sitting, standing, walking and none (for no proposed vision-based deep learning methods in an occupant present) were selected. The training dataset actual office environment and assessed its performance consisted of a total of 500 images and 662 labels. For in terms of energy savings and indoor environment each of the images, manual image labelling was quality. Finally, the heat emission profiles generated can performed by assigning bounding boxes around the also be used as input for building energy simulation (BES) specific region of interests. tools which can increase the reliability of results since unpredictability of occupant behaviour is one of the CNN was selected as the main type of network parameters which creates difficulties in BES. Hence, the architecture. CNN is designed to establish applications study aims to develop a vision-based deep learning for computer vision-related tasks with image datasets method for real-time occupancy activity detection and [30]. It has been extensively used for deep learning recognition. object detection projects. The provision of pre-existing open-source deep learning-based models by 2. METHOD TensorFlow, such as the CNN TensorFlow object detection API (application interface) [13] was utilised as The development of the proposed framework has the base configuration for detection-based applications. three main sections. Section 1 is the development and This object detection model is part of the TensorFlow application of a deep learning model for occupancy pre-defined model’s repository; it consists of the activity detection and recognition. The model is based on incorporation of high levels API’s and includes the ability a trained and validated convolutional neural network to localise and identify multiple objects in a single image. (CNN) which is deployed to an AI-powered camera. Therefore, the TensorFlow platform with the CNN Section 2 is the formation of the deep learning TensorFlow object detection API was employed for the influenced profiles (DLIP) using the live occupancy development of a suitable model for occupancy detection. The DLIP can be fed into building energy detection. 2 Copyright © 2020 ICAE To train the CNN model, the network architecture on one side of the room to enable the detection of layers, and training options were defined by applying a occupancy situated on the opposite side. A 1080p transfer learning approach. The COCO-trained model of camera with a wide 90-degree field of view was operated Faster R-CNN (With Inception V2) was selected and using the trained deep learning modelThe building is configured to train the desired occupancy activity equipped with natural ventilation along with simple air- detection model. The architecture and the pipeline of the conditioning system to provide an internal set point model used is given in Figure 2. temperature maintained at 21°C. The Nottingham, UK weather data was inputted into building energy simulation model. Standard occupancy profiles with a sensible and latent heat gain of 70W and 45W per person was assigned [14]. A range of activities was performed by the occupants during the rea-time detection experiment. The output data for each of the detected occupants were Fig 2 Convolutional Neural Network (CNN) based deep used to form the occupancy heat emission profiles learning model configured for the training of the model for (DLIP). Figure 4 presents an example process of DLIP occupancy activity detection and recognition formation. Detected occupancy activity
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