Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

Vivi, QL; Parlikad, Ajith Kumar; Woodall, Philip; Ranasinghe, Gishan Don; Heaton, James T.

doi:10.17863/cam.38523

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Finally, AI-powered predictive maintenance in renewable energy seamlessly integrates sensor data, historical records, and weather patterns. Through the application of Machine Learning, Deep Learning, and Digital Twins, this approach offers a proactive solution to equipment failures, ultimately contributing to increased reliability, extended equipment lifespan, and optimized maintenance costs in the renewable energy sector (Vivi et al, 2019).…”

Section: Ai Techniques For Predictive Maintenancementioning

confidence: 99%

AI in renewable energy: A review of predictive maintenance and energy optimization

Ahmad Hamdan,

Kenneth Ifeanyi Ibekwe,

Valentine Ikenna Ilojianya

et al. 2024

Int. J. Sci. Res. Arch.

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In the dynamic landscape of the burgeoning renewable energy sector, optimizing energy output, ensuring robust infrastructure maintenance, and seamless integration into the grid present formidable challenges. This paper delves into the transformative potential of artificial intelligence (AI) as a solution to these critical issues. The focus of this study is on the current state of AI applications within the renewable energy domain, particularly honing in on its profound impact on predictive maintenance and energy optimization across diverse sources such as solar, wind, and hydro. By examining the underlying AI techniques employed in this context, the research seeks to unravel the intricacies of how AI contributes to enhancing the efficiency and sustainability of renewable energy systems. A critical component of this exploration involves the analysis of successful case studies, illustrating real-world applications where AI has made substantial strides in predictive maintenance and energy optimization. These cases provide tangible evidence of the practical implications of incorporating AI into renewable energy practices. The research explores AI’s role in renewable energy, focusing on emerging trends and future directions. It aims to understand AI’s transformative influence on optimization, sustainability, and energy efficiency, fostering a more resilient and efficient energy landscape. AI is revolutionizing the renewable energy sector, transforming infrastructure maintenance, energy generation optimization, and integrating renewable sources into the grid. Its advanced analytics, predictive capabilities, and optimization are crucial in achieving global renewable energy targets. As AI technology evolves, its impact on the renewable energy landscape will deepen, paving the way for a cleaner, more sustainable future. By harnessing AI’s power, we can accelerate the transition towards a renewable energy future, ensuring a thriving planet for future generations.

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Section: Ai Techniques For Predictive Maintenancementioning

confidence: 99%

AI in renewable energy: A review of predictive maintenance and energy optimization

Ahmad Hamdan,

Kenneth Ifeanyi Ibekwe,

Valentine Ikenna Ilojianya

et al. 2024

Int. J. Sci. Res. Arch.

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Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance

Xie

Rodenas-Herráiz

et al. 2020

ECAM

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PurposeVisual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approachThe developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.FindingsTaking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/valueThe originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.

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Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

Cited by 2 publications

References 27 publications

AI in renewable energy: A review of predictive maintenance and energy optimization

AI in renewable energy: A review of predictive maintenance and energy optimization

Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance

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