Artificial intelligence as an agent to transform research paradigms in building science and technology

Choi, Joon-Ho; Yang, Bin; Yu, Chuck Wah

doi:10.1177/1420326x211017694

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Building management should use AI for numerous reasons. First, AI technologies can improve building operation and management, reducing energy usage and improving automation, control, and reliability [7]- [9]. Second, AI can improve environmental controls and building performance to improve occupant safety and comfort [8], [10].…”

Section: Sustainable Energy Managementmentioning

confidence: 99%

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Hanafi,

Moawed,

Abdellatif

2024

Engineering Research Journal (Shoubra)

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This paper explores artificial intelligence's (AI) transformative potential in optimizing energy management within buildings, aligning with environmental objectives and sustainable practices. AI-based methodologies are pivotal in identifying inefficiencies, forecasting future energy requirements, and mitigating energy wastage. Adopting AI-driven energy management systems enhances efficiency, reduces costs, and contributes to a decreased building environmental footprint. Furthermore, AI empowers buildings to actively participate in energy markets by accurately predicting real-time supply and demand without operational disruption. The study delves into various AI applications, including energy prediction, optimization, fault detection and diagnosis (FDD), and real-world implementations. Notably, AI's role in fault detection and diagnostics is highlighted, emphasizing its substantial contribution to diagnostic precision. Specific numerical outcomes from reviewed studies underscore the tangible impact of AI techniques. Predictive control powered by AI achieved a remarkable 20% reduction in heating energy without compromising comfort. Additionally, smart home energy management algorithms demonstrated a notable 22.63% decrease in electricity costs and a 22.77% reduction in the peak-to-average ratio. These concrete figures underscore the practical success of AI techniques in significantly reducing energy consumption. This review affirms the transformative potential of AI in building energy management. Including specific numerical values from empirical studies adds a quantitative dimension to the discussion, providing clear evidence of the positive impact of AI on energy efficiency.

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Section: Sustainable Energy Managementmentioning

confidence: 99%

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Hanafi,

Moawed,

Abdellatif

2024

Engineering Research Journal (Shoubra)

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“…Recently, many research attempts have been made using advanced technologies in the field of computer science such as Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT) in building operations. These technologies are enabling use cases such as model predictive control, system fault detection and diagnosis, occupancy estimation and detection, demand response and more in general they are system integrators of different building subsystems and occupants [4,5]. The mentioned technologies can help to significantly improve building performance, from better indoor climate and lower energy consumption to better space efficiency.…”

Section: Introductionmentioning

confidence: 99%

Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor

2022

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An important instrument for achieving smart and high-performance buildings is Machine Learning (ML). A lot of research has been done in exploring the ML models for various applications in the built environment such as occupancy prediction. Nevertheless, the research focused mostly on analyzing the feasibility and performance of different supervised ML models but has rarely focused on practical applications and the scalability of those models. In this study, a transfer learning method is proposed as a solution to typical problems in the practical application of ML in buildings. Such problems are scaling a model to a different building, collecting ground truth data necessary for training the supervised model, and assuring the model is robust when conditions change. The practical application examined in this work is a deep learning model used for predicting room occupancy using indoor climate IoT sensors. This work proved that it is possible to significantly reduce the length of ground truth data collection to only two days. The robustness of the transferred model was tested as well, where performance stayed on a similar level if a suitable normalization technique was used. In addition, the proposed methodology was tested with room occupancy level prediction, showing slightly lower performance. Finally, the importance of understanding the performance metrics is crucial for market adoption of ML-based solutions in the built environment. Therefore, in this study, additional analysis was done by presenting the occupancy prediction model performance in understandable ways from the practical perspective.

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Artificial Intelligence and I4.0 in Manufacturing: The Role of Sustainability

Cardoso,

Ares,

Ferreira

et al. 2023

Lecture Notes in Mechanical Engineering

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Artificial intelligence as an agent to transform research paradigms in building science and technology

Cited by 5 publications

References 37 publications

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Advancing Sustainable Energy Management: A Comprehensive Review of Artificial Intelligence Techniques in Building

Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor

Artificial Intelligence and I4.0 in Manufacturing: The Role of Sustainability

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