Occupancy Detection for Smart HVAC Efficiency in Building Energy: A Deep Learning Neural Network Framework using Thermal Imagery

Acquaah, Yaa; Steele, Jonathan; Gokaraju, Balakrishna; Tesiero, Raymond; Monty, Gregory H.

doi:10.1109/aipr50011.2020.9425091

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…The value of buildings constitutes 50% of the national wealth and 13% of the GDP comes from the construction sector. Per capita consumption in the United States is about 10 tons, of which 90% is discarded as waste, and despite the technology used in this sector, the 1996 report showed that there is 40% scope for saving and improvement in construction works [21,22]. Any reform that takes place in the construction sector and is in the direction of technicalizing the affairs will be an important contribution to the industrialization of the sector.…”

Section: B-developments In the Attitude To Improve The Environmentmentioning

confidence: 99%

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

2024

nano-ntp

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Non-intrusive indoor environment sensing for occupancy detection and estimation has attracted extensive research interest in the building domain over the past decade due to the increasing number of applications for improving building infrastructure. Occupancy detection and estimation can be integrated into building appliances to manage lighting applications, intrusion detection in secured building areas, and occupancy-driven ventilation which has the potential to improve the performance of the Heating Ventilation and air-conditioning (HVAC) system through the finegrained occupant count to enhance the trade-off between thermal comfort and energy consumption. The research strategies for occupancy detection and estimation have utilized different technologies (including camera, wearable, and indoor environmental variables sensing through direct sensing and machine learning), which experience challenges in terms of acquiring essential sensory data related to occupancy information and correctly modeling the occupancy data due to hardware deployment limitations and underlying cost. This study explores existing technologies and methods for occupancy detection and estimation with their underlying issues. It provides a comprehensive procedure for occupancy modeling methodology using different machine learning methods and analyzing their comparative results to assist in decision making for choosing an optimal technique for solving occupancy detection and estimation problem. The results recommend Random Forest as a candidate model with high performance achieving 73.6% to 99.7% for occupancy detection and overall, 99.3% for occupancy estimation.

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Section: B-developments In the Attitude To Improve The Environmentmentioning

confidence: 99%

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

2024

nano-ntp

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“…Because of this, numerous scholars have developed specialized models of thermal comfort [11], which may be used to regulate the indoor environment [12] and get higher precision using more precise variables. A number of physiology have been associated with a person's level of thermal relief, as demonstrated by these specific models [11]; the factors that were most frequently used to evaluate temperature sensation were body temperature [13], pulse [14], respiration [15], appearance. Temperature [16], and humidity [13].…”

Section: Smart Hvac Systemmentioning

confidence: 99%

Optimizing the Future: Critical Review on Smart Building Contraction Technologies

Abdelmjeed,

Hassan

2024

IJLTEMAS

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In order to enhance a building's functionality and energy efficiency, smart construction technology combines controlled reliable controllers and programs with connected sensors, smart energy products, and data analytics software to track ambient data and resident energy usage patterns. The integration of intelligent technologies and controllers in the construction sector is steadily expanding as a result of their growing significance in the fabrication, business-related; it and scholarly domains. This investigation uses a systematic strategy to review the related published literature between 2013 and 2023.Prior to investigating the rating of publications based on quality evaluation and yearly trend publication was done. The study further classifies the literature based on integration technologies and application. The finding reveals that most of the literature features with occupants an interface to schedule, monitor, and adjust energy consumption profiles. These developments also enable utilities to engage in an exchange with the grid by means of demand response strategies and automatic feedback interruption features.Potential study directions are also examined in the paper, particularly with regard to seamless integration, privacy, and security enhancements. It is also proposed that continuous surveillance of data and machine learning are two ways to enhance the intelligence of smart building technology.

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“…A controller proposed in [41] combined camera-based image and video processing with the help of computer vision (CV) libraries and estimated total occupancy numbers through headcount or indoor object tracking. A similar approach in [42] used single-camera occupancy detection to control ventilation in the lecture theater through a headcount estimation procedure.…”

Section: Existing Predictive Controlmentioning

confidence: 99%

Adaptive HVAC System Based on Fuzzy Controller Approach

Abuhussain,

Alotaibi,

Aliero

et al. 2023

Applied Sciences

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Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

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Occupancy Detection for Smart HVAC Efficiency in Building Energy: A Deep Learning Neural Network Framework using Thermal Imagery

Cited by 12 publications

References 21 publications

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

Building Occupancy Detection for Energy-Saving: Exploring the Current Technologies and Methods with their Underlying Issues

Optimizing the Future: Critical Review on Smart Building Contraction Technologies

Adaptive HVAC System Based on Fuzzy Controller Approach

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