Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM
Introduction
According to relevant statistics, the annual growth rate of the global construction footprint from 2005 to 2020 reached 6.92% [1]. However, the energy intensity of the construction sector has only continued to decline by 0.5% to 1% per year since 2010, which is far lower than the urban area expansion rate and the construction area growth rate [2]. This has led to construction energy consumption that has accounted for 40% of the world's total energy consumption [3]. Therefore, reducing the source demand of buildings is essential to alleviate the pressure and sustainability of the global energy supply [4]. One of the methods is to explore the energy-saving potential of lighting systems, which is of great significance for reducing the overall energy consumption of buildings. This is because lighting systems consume about 20% to 60% of the energy used in office buildings and rank second to the ventilation system [5], [6].
Under the conditions of ensuring the illuminance scale and lighting quality, improving the energy conversion efficiency of lighting systems has become a crucial research area of green building energy conservation [7], [8], [9]. At present, the commonly used energy-saving methods for lighting systems include the use of sensor control and LED lights instead of manual switch control and fluorescent lights [10], [11], [12], [13]. Although these methods can effectively reduce the energy consumption of lighting systems, they also cause a substantial increase in the initial installation cost [14]. The biggest drawback of these lighting systems is the inability to realize intelligent and integrated management, which presents huge difficulties for the whole life cycle O&M as well as energy consumption prediction.
To solve the limitations of existing lighting systems, more and more attention has been paid to integrating indoor intelligent lighting systems. For example, some scholars have begun to combine the Internet of Things (IoT) and sensors to complete more complex intelligent control and enhance the ability of lighting systems to interact with the environment [15], [16]. Although the current research can solve the problem of intelligent control to a certain extent, there is still a lack of methods to realize visual management and intelligent control of lighting systems in the whole process. Widespread applications of computer vision and digital twins offer alternatives to address the above difficulties. The advantages of computer vision include long-distance, non-contact, high-precision, and high-efficiency data collection, which can help in developing in-depth, whole-process intelligent decision-making control of lighting systems. Michael and Jon [17] coined the term “digital twin” in 2003. Since then, its popularity has grown and is now recognized as a key enabler of the transition to Industry 4.0. A digital twin can reflect a real-world built environment in a virtual space, and can also simulate in real time the processes connected throughout its entire life cycle, performing diagnostic and predictive analytics for O&M [18]. In addition, some scholars have developed lightweight web clients for digital twin systems based on WebGL and the Internet, which further promotes remote browsing and sharing of digital assets and provides a remote visualization, integration and collaborative management platform for digital twin systems [19].
Considering the advantages of computer vision and digital twin, this paper proposes to combine both lighting and surveillance systems to establish a VO&M platform for a DTL system. The originality and contributions of this paper are highlighted as follows: (1) provides an in-depth, full-process, and intelligent control decisions method for a lighting system; (2) formed the digital twin asset of a lighting system, which can provide remote visual management; (3) used big data analysis technology to analyze O&M data to provide a data-driven platform for realizing an integrated management of the entire life cycle of green smart buildings and lighting equipment; (4) verified that the multi-source heterogeneous data fusion of surveillance system, lighting system, and BIM is helpful to improve indoor energy efficiency for intelligent buildings.
The remainder of this paper is organized as follows. Section 2 reviews the literature on lighting systems, BIM, and computer vision. Section 3 discusses the proposed method in detail. Section 4 presents a case study to verify the effectiveness of the proposed method. Section 5 includes conclusions and future work.
Section snippets
Energy saving for lighting systems
Presently, several energy-saving research focused on light source and control systems have been conducted and achieved excellent results. For example, the use of new LED lights saved 10%-25% of lighting energy consumption [20], and the use of sensor control saved more than 50% of lighting energy consumption. Additionally, Juntunen et al. [21] use passive infrared (PIR) sensors to intelligently track the movement of pedestrians and dynamically controlled lighting devices, which saved more than
Methodology
This research combined an office building's existing surveillance system and lighting system and proposed a new digital twin-based intelligent lighting system, which can adaptively provide lighting quality that is fit for purpose and minimizes energy consumption. The proposed cloud-based system can be divided into four parts, including a realistic lighting system, detection equipment, digital assets and operation and maintenance as shown in Fig. 1.
Case study
This research used 14 days of a surveillance video stream for experiments to verify the effectiveness of the proposed method. The period is from 2020.12.02 to 2020.12.15, which is the normal school time, including Monday to Sunday; hence, it has a certain degree of representativeness. The experimental scene is a long corridor with two cameras (Fig. 14). The corridor lacks natural lighting and relies on artificial lighting to meet its lighting demand. The energy consumption of this corridor
Conclusions
Through the combination of computer vision and BIM, this paper proposes a DTL system, which provides in-depth, full-process and intelligent decision-making on lighting control. Additionally, it contributes to the collaborative management of lighting system, which can result in reducing energy consumption and electricity costs. A dynamic BIM platform based on Three.js has also been created, which can reduce O&M time and costs. In addition, the digital twin assets of the intelligent lighting
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research is supported by the Shenzhen Science and Technology Project(No. JSGG20210802153801004), the Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (FDYT) (No. 2020KQNCX060) and the Foundation for Basic and Applied Basic Research of Guangdong Province (No. 2020A1515111189).
Statement of dataset use
This paper created a novel pedestrian dataset by combining the USC public pedestrian dataset and self-made video stream key-frames. We are grateful to USC for opening access to the pedestrian dataset to other researchers. The ownership of the USC dataset belongs to the original author. This paper only uses it to complete the training of the model, and does not make any changes to the dataset. The ownership of the self-made video streaming dataset belongs to the author of this paper, and has the
References (75)
- et al.
A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings
Appl. Energy
(2019) - et al.
A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies
Appl. Energy
(2015) - et al.
The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model
Appl. Energy
(2017) - et al.
Design and optimization of a novel electrowetting-driven solar-indoor lighting system
Appl. Energy
(2020) - et al.
Analysis and comparison of lighting design criteria in green building certification systems —Guidelines for application in Serbian building practice
Energy Sustain. Dev.
(2014) Light level, visual comfort and lighting energy savings potential in a green-certified high-rise building
J. Build. Eng.
(2020)Improving present-day energy savings among green building sector in Malaysia using benefit transfer approach: Cooling and lighting loads
Renew. Sustain. Energy Rev.
(2021)- et al.
Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches
Energy Build.
(2015) - et al.
Lighting system control techniques in commercial buildings: Current trends and future directions
J. Build. Eng.
(2020) - et al.
Energy savings in greenhouses by transition from high-pressure sodium to LED lighting
Appl. Energy
(2021)
Energy-maintenance optimization for retrofitted lighting system incorporating luminous flux degradation to enhance visual comfort
Appl. Energy
Development of a prototype smart home intelligent lighting control architecture using sensors onboard a mobile computing system
Energy Build.
Indoor intelligent lighting control method based on distributed multi-agent framework
Optik
IoT-Cognizant cloud-assisted energy efficient embedded system for indoor intelligent lighting, air quality monitoring, and ventilation
Internet Things
Design and implementation of a smart infrastructure digital twin
Autom. Constr.
Web-based digital twin modeling and remote control of cyber-physical production systems
Rob. Comput. Integr. Manuf.
Design of an energy-saving controller for an intelligent LED lighting system
Energy Build.
Smart and dynamic route lighting control based on movement tracking
Build. Environ.
Lighting energy savings in offices using different control systems and their real consumption
Energy Build.
A wireless sensor network based on the novel concept of an I-matrix to achieve high-precision lighting control
Build. Environ.
Distributed lighting control with daylight and occupancy adaptation
Energy Build.
A new optimal light sensor placement method of an indoor lighting control system for improving energy performance and visual comfort
J. Build. Eng.
A fuzzy-logic IoT lighting and shading control system for smart buildings
Autom. Constr.
Design of Office Intelligent Lighting System Based on Arduino
Procedia Comput. Sci.
Understanding cities with machine eyes: A review of deep computer vision in urban analytics
Cities
Special issue on role of computer vision in smart cities
Image Vis. Comput.
A novel face recognition technology to enhance health and safety measures in hospitals using SBC in pandemic prone areas
Mater. Today:. Proc.
Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock
Comput. Commun.
Smart attendance using deep learning and computer vision
Mater. Today:. Proc.
Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method
Appl. Energy
Prediction and analysis of heating energy demand for detached houses by computer vision
Energy Build.
Lighting Fitting Controller Using Image Processing System
IFAC Proc.
Lighting control system based on digital camera for energy saving in shop windows
Energy Build.
Characterization of a quasi-real-time lighting computing system based on HDR imaging
Energy Procedia
On-site monitoring and subjective comfort assessment of a sun shadings and electric lighting controller based on novel High Dynamic Range vision sensors
Energy Build.
Research on library lighting intelligent control based on infrared image processing techniques
Optik
Energy efficient intelligent light control with security system for materials handling warehouse
Mater. Today:. Proc.
Cited by (26)
Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions
2024, Energy and Built EnvironmentTowards a digital twin architecture for the lighting industry
2024, Future Generation Computer SystemsReview of the building energy performance gap from simulation and building lifecycle perspectives: Magnitude, causes and solutions
2024, Developments in the Built EnvironmentAdvancing construction site workforce safety monitoring through BIM and computer vision integration
2024, Automation in ConstructionDeveloping an integrative framework for digital twin applications in the building construction industry: A systematic literature review
2024, Advanced Engineering InformaticsAn adaptive crack inspection method for building surface based on BIM, UAV and edge computing
2024, Automation in Construction