IOT and AI-based Energy Monitoring and Control System for NZEB

Ahmed, Zubair; Islam, M. S.; Dudhe, Ravishankar; More, Bhakti; Baig, Mirza Humayun

doi:10.1109/aset56582.2023.10180878

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Buildings can adjust their energy consumption based on real-time data from the grid, contributing to overall grid stability and reliability. Automation systems can enhance occupant comfort by changing environmental conditions based on individual preferences and real-time needs [56]. Support of IoT and AI will enhance the residents' comfort and satisfaction level and improve their ease of living and quality of life.…”

Section: Energy Monitoring and Managementmentioning

confidence: 99%

Pathway to Sustainability: An Overview of Renewable Energy Integration in Building Systems

Reddy,

Hariram,

Ghazali

et al. 2024

Sustainability

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Decarbonizing the building sector is crucial for mitigating climate change, reducing carbon emissions, and achieving an energy production–consumption balance. This research aims to identify key design principles and strategies to enhance energy savings and analyze the integration potential of renewable energy sources (RES) such as solar, wind, geothermal, and biomass, providing in-depth technical exploration and evaluating current building developments. Moreover, the study also examines recent developments, explicitly focusing on integrating hybrid renewable energy systems, energy storage solutions, and AI-based technological innovations. Through comprehensive analysis and critical evaluation, this research provides valuable insights and practical recommendations for achieving building sustainability and advancing the transition towards a low-carbon built environment.

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Section: Energy Monitoring and Managementmentioning

confidence: 99%

Pathway to Sustainability: An Overview of Renewable Energy Integration in Building Systems

Reddy,

Hariram,

Ghazali

et al. 2024

Sustainability

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Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda

Elghaish,

Matarneh,

Hosseini

et al. 2024

SASBE

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PurposePredictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.Design/methodology/approachThis study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.FindingsFindings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.Practical implicationsThe results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.Originality/valueThis paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

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