Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy...

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Autores principales:	Paige Wenbin Tien, Shuangyu Wei, John Calautit, Jo Darkwa, Christopher Wood
Formato:	article
Lenguaje:	EN
Publicado:	SDEWES Centre 2021
Materias:	artificial intelligence deep learning energy management occupancy detection activity detection hvac system. Technology T Economic growth, development, planning HD72-88
Acceso en línea:	https://doaj.org/article/666ffb3b601a4ba6b268a36b46d25813
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id	oai:doaj.org-article:666ffb3b601a4ba6b268a36b46d25813
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spelling	oai:doaj.org-article:666ffb3b601a4ba6b268a36b46d258132021-11-20T09:32:48ZOccupancy heat gain detection and prediction using deep learning approach for reducing building energy demand1848-925710.13044/j.sdewes.d8.0378https://doaj.org/article/666ffb3b601a4ba6b268a36b46d258132021-09-01T00:00:00Z http://www.sdewes.org/jsdewes/pid8.0378 https://doaj.org/toc/1848-9257The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems.Paige Wenbin TienShuangyu WeiJohn CalautitJo DarkwaChristopher WoodSDEWES Centrearticleartificial intelligencedeep learningenergy managementoccupancy detectionactivity detectionhvac system.TechnologyTEconomic growth, development, planningHD72-88ENJournal of Sustainable Development of Energy, Water and Environment Systems, Vol 9, Iss 3, Pp 1-31 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence deep learning energy management occupancy detection activity detection hvac system. Technology T Economic growth, development, planning HD72-88
spellingShingle	artificial intelligence deep learning energy management occupancy detection activity detection hvac system. Technology T Economic growth, development, planning HD72-88 Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
description	The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems.
format	article
author	Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood
author_facet	Paige Wenbin Tien Shuangyu Wei John Calautit Jo Darkwa Christopher Wood
author_sort	Paige Wenbin Tien
title	Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
title_short	Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
title_full	Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
title_fullStr	Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
title_full_unstemmed	Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
title_sort	occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
publisher	SDEWES Centre
publishDate	2021
url	https://doaj.org/article/666ffb3b601a4ba6b268a36b46d25813
work_keys_str_mv	AT paigewenbintien occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT shuangyuwei occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT johncalautit occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT jodarkwa occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand AT christopherwood occupancyheatgaindetectionandpredictionusingdeeplearningapproachforreducingbuildingenergydemand
_version_	1718419428959322112

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

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