Deep and transfer learning for building occupancy detection: A review and comparative analysis

Sayed, Aya; Himeur, Yassine; Bensaali, Fayçal

doi:10.1016/j.engappai.2022.105254

Cited by 79 publications

(49 citation statements)

References 114 publications

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“…Sayed, Himeur, and Bensaali investigate the development of the internet of things (IoT) for energy consumption [15]. They are concerned with optical/thermal cameras, smart meters, and environmental sensors such as carbon dioxide (CO2) and passive infrared (PIR) for gathering occupancy data.…”

Section: A Existing Surveysmentioning

confidence: 99%

“…The system for detecting human bodies is currently being refined. Through machine learning, neural networks, and other algorithms based on the original CSI data, you can obtain more accurate findings.• The goal of this project15 is to make it possible for the ESP32 WiFi equipped micro-controller to gather CSI.…”

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confidence: 99%

“…13 available at https://github.com/ChunjingXiao/DeepSeg14 Available at https://github.com/espressif/esp-csi15 Available at https://github.com/StevenMHernandez/ESP32-CSI-Tool16 Available at https://github.com/ludlows/CSI-Activity-Recognition17 Available at https://github.com/kdkalvik/WiFi-user-recognition18 Available at https://github.com/Y0Username/iotMonitor19 Available at https://github.com/lukasmalkmus/homekit-ble-occupancy…”

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confidence: 99%

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Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Shahbazian

Trubitsyna

2023

IEEE Access

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Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decisionmaking that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wirelessbased human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

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Section: A Existing Surveysmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Shahbazian

Trubitsyna

2023

IEEE Access

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“…Transfer learning 27 is a machine learning method that aims to transfer knowledge learned in one task to a new task in order to improve the performance of the model on the new task. The goal of transfer learning is to leverage the knowledge learned in the source domain to help solve problems in the target domain.…”

Section: Transfer Learningmentioning

confidence: 99%

Artificial Intelligence Carbon Neutrality Strategy in Sports Event Management based on STIRPAT-GRU and Transfer Learning

Zhang

2023

Preprint

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With the increasing concern about carbon emissions and climate change, carbon neutrality has become a crucial issue in various domains, including sports event management. Artificial intelligence has shown great potential in addressing environmental challenges and improving sustainability. To develop AI-powered carbon neutrality strategies for sports event managementthe ,this paper proposed a method based on STIRPAT-GRU and transfer learning. The primary objective of this research is to analyze the impact of population, wealth, and technology on carbon emissions in sports event management and to predict future trends and patterns of carbon emissions. Firstly, the STIRPAT model is used to identify the key drivers of carbon emissions in sports events. Then, the GRU neural network is then used to develop a predictive model to forecast future trends and patterns of carbon emissions. Finally, transfer learning is used to improve the accuracy and robustness of the model by leveraging knowledge and experience gained from other domains.The experimental results show that the proposed approach outperforms other methods, demonstrating its effectiveness in predicting carbon emissions in sports events and developing a carbon neutrality strategy. The significance of this research lies in its potential to provide sports event managers with a data-driven approach to manage carbon emissions, leading to a more sustainable and environmentally friendly sports industry.

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“…2020 ; Sayed et al. 2022 ). The data can be directly used as inputs to control and regulate some of the buildings’ equipment, such as lights, air conditioning, doors, etc.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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Deep and transfer learning for building occupancy detection: A review and comparative analysis

Cited by 79 publications

References 114 publications

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Artificial Intelligence Carbon Neutrality Strategy in Sports Event Management based on STIRPAT-GRU and Transfer Learning

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

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