Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

Wei, Shuangyu; Calautit, John Kaiser

doi:10.1002/er.6306

Cited by 10 publications

(8 citation statements)

References 45 publications

(73 reference statements)

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“…Overall, the fire detection accuracy was 7% lower than Zhang et al, 14 while the smoke was significantly less accurate than Kong et al 15 The results suggest the performance is variable and heavily situationally dependent, and the environmental setting can impact the detection performance. It should be acknowledged that the existing solutions mentioned in Table 5 were designed to enable the detection of both fire and smoke within outdoor settings.…”

Section: Analysis Of Detection Performancementioning

confidence: 60%

“…Early detection of fire and smoke is important for residential spaces such as bedrooms where there is typically only one exit, and the person may be asleep or on medication. This study will build on the previous works by Tien et al 12,13 and Wei et al, 14,15 where a vision-based artificial intelligence (AI) approach was used to detect and recognise the usage of indoor spaces for aiding demand-driven control systems.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Development and evaluation of a vision-based transfer learning approach for indoor fire and smoke detection

Pincott

Tien

Wei

et al. 2022

Building Services Engineering Research and Technology

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Fire poses a significant risk across industrial and domestic settings, especially to firefighters who must tackle the blaze. Current technology for detection in indoor environments are smoke detectors and flame detectors. However, these detectors have several limitations during the ignition phase of a fire and propagation. These systems cannot detect an exact position of the fire nor how the fire is spreading or its size, all of which is necessary information for fire services when dealing with these incidents. A potential solution is to use artificial intelligence techniques such as computer vision, which has shown the potential to detect and recognise objects and activities in indoor spaces. This study aims to develop a vision-based fire and smoke detection system. A deep learning technique that incorporates convolutional neural networks (CNN) was utilised to develop the real-time detection approach that can potentially provide necessary information for fire services, including identifying the position and size of the fire and how the fire spreads. A transfer learning approach using a pre-trained model was used to train the detector. Based on the detection and recognition tests using indoor fire and smoke videos, results indicated that the fire detection achieved up to 92.37% correct detections while the smoke detection did not perform as well. Hence, further improvement and evaluation of the detection approach will be conducted in future work, focusing on the impact of different parameters such as the detection model, building type, indoor space size and positioning of the detection camera. The present study provides an insight into the capabilities and potential applications of the concept.

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Section: Analysis Of Detection Performancementioning

confidence: 60%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Development and evaluation of a vision-based transfer learning approach for indoor fire and smoke detection

Pincott

Tien

Wei

et al. 2022

Building Services Engineering Research and Technology

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“…For example, the current design project is to operate at the maximum occupancy rate of the space at all times, ignoring the actual occupancy rate of the space, and fix the fresh air supply ratio at a certain value, such as 15%-30%. As a result, unused or completely unoccupied space is regulated by the air conditioning system, resulting in rooms being under-conditioned or overconditioned and potentially a large amount of wasted energy [4].…”

Section: Introductionmentioning

confidence: 99%

A pilot study of occupant centric control stratum ventilation based on computer vision

Liu

Yang²,

Lin³

2022

E3S Web Conf.

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Indoor occupant information has an obvious influence on operating parameters of heating ventilation and air conditioning (HVAC) system, which further affects occupants’ thermal comfort and energy consumption. This pilot study proposes an occupant centric control (OCC) strategy for stratum ventilation (SV) to achieve demand control ventilation (DCV). Firstly, the computer vision sensing system and deep learning algorithm are used to detect the number of occupants in real time, and the accuracy of the number of occupants in the office environment was evaluated. Then, the occupant centric stratum ventilation control strategy is designed by the dynamic changes of cooling load. Finally, the thermal comfort and air quality of the thermal environment created by the OCC strategy were evaluated through subject experiment, and the energy consumption of the HVAC system was calculated in combination with the energy consumption simulation software. This study adjusts system setting values according to actual needs, so that the HVAC system responds to the dynamic changes of the indoor cooling load in real time, creating a comfortable and healthy indoor environment in an energy efficient manner.

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“…The present work will develop an approach based on computer vision and deep learning to detect and estimate the equipment's usage in a building space in real-time. Based on previous works, 16,17 a technique using the Faster regions with CNN features (RCNN) is proposed to detect and recognise equipment usage in an office space. The model will be trained and deployed to a standard camera, and field tests will be carried out in an office space in a building at the university.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

The impact of deep learning–based equipment usage detection on building energy demand estimation

Wei

Tien

et al. 2021

Building Services Engineering Research and Technology

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As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

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Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

Cited by 10 publications

References 45 publications

Development and evaluation of a vision-based transfer learning approach for indoor fire and smoke detection

Development and evaluation of a vision-based transfer learning approach for indoor fire and smoke detection

A pilot study of occupant centric control stratum ventilation based on computer vision

The impact of deep learning–based equipment usage detection on building energy demand estimation

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