Multi-Source Domain Adaptation Using Ambient Sensor Data DOI Creative Commons
Jawher Dridi, Manar Amayri, Nizar Bouguila

et al.

Applied Artificial Intelligence, Journal Year: 2024, Volume and Issue: 38(1)

Published: Nov. 19, 2024

Smart buildings have gained increasing interest recently by providing several advanced solutions, especially AI-based solutions. Activity recognition and occupancy estimation are among the outcomes of smart that can help provide advantages such as energy management security Previously, domain adaptation (DA) has been widely considered researchers to transfer knowledge from source domains, where we abundant labeled data, a target data is scarce. It tedious time-consuming task label with building applications which why unsupervised DA do in unlabeled domain. Semi-supervised (SSDA) also small amount Most (UDA) SSDA methods one target. However, it possible exploit multiple domains instead single enhance performance Multi-source (MSDA) more difficult than single-source but efficient. In this research, adapt MDSA evaluate them using sensorial datasets.

Language: Английский

Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis DOI Creative Commons
Jingyi Liu, J.F. Chen

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 994 - 994

Published: March 21, 2025

With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. Utilizing tools such as VOSviewer and Bibliometrix, this study systematically reviews body related literature, focusing on key emerging trends cutting-edge ML techniques, including deep learning, reinforcement unsupervised optimizing performance managing carbon emissions. First, paper delves into role prediction, intelligent management, sustainable design, with particular emphasis how smart systems leverage real-time data analysis prediction to optimize usage significantly reduce emissions dynamically. Second, summarizes technological evolution future sector identifies critical challenges faced by field. The findings provide a technology-driven perspective for advancing sustainability construction industry offer valuable insights research directions.

Language: Английский

Citations

1
Industry 5.0, Towards an Enhanced Built Cultural Heritage Conservation Practice DOI Creative Commons
Alejandro Jiménez Ríos, Margarita L. Petrou, Rafael Ramírez Eudave

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110542 - 110542

Published: Aug. 23, 2024

The rise of Industry 4.0 has led to a rapid increase in digitalization and industrial operations. However, it recently been deemed insufficient fulfilling European objectives for 2030. In response, counteract the unintended negative consequences triggered by 4.0, 5.0 introduced. purpose this article is shed light on how architecture, engineering, construction, management, operation, conservation industry can adapt better prepare embrace novel principles enabling technologies, ultimately resulting enhanced practices built cultural heritage environment. To achieve this, systematic literature review was conducted following PRISMA methodology. principal results highlight work different professionals our views potential enhancing practices. Major conclusions indicate that artificial intelligence digital twins are two most studied technologies field. Sustainability broadly discussed throughout analyzed literature, whereas resilience human centrism require further research implementation efforts holistic adoption. significant scientific novelty lies comprehensive scope terms with particular emphasis buildings. Thus, valuable practitioners seeking best practices, policymakers as suggests ways encourage adoption conservation, researchers highlights gaps stimulates paths innovation.

Language: Английский

Citations

7
Innovative AI Strategies for Enhancing Smart Building Operations Through Digital Twins: A Survey DOI
Adel Oulefki, Hamza Kheddar, Abbes Amira

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115567 - 115567

Published: March 1, 2025

Language: Английский

Citations

1
Generalized harmonic fuzzy partition C-means clustering DOI
Chengmao Wu,

Siyu Zhou

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)

Published: Jan. 27, 2025

Language: Английский

Citations

0
Explainable domain adaptation for imbalanced occupancy estimation DOI

Naailah Mahamoodally,

Jawher Dridi, Manar Amayri

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110613 - 110613

Published: Sept. 1, 2024

Citations

1
Multi-Source Domain Adaptation Using Ambient Sensor Data DOI Creative Commons
Jawher Dridi, Manar Amayri, Nizar Bouguila

et al.

Applied Artificial Intelligence, Journal Year: 2024, Volume and Issue: 38(1)

Published: Nov. 19, 2024

Smart buildings have gained increasing interest recently by providing several advanced solutions, especially AI-based solutions. Activity recognition and occupancy estimation are among the outcomes of smart that can help provide advantages such as energy management security Previously, domain adaptation (DA) has been widely considered researchers to transfer knowledge from source domains, where we abundant labeled data, a target data is scarce. It tedious time-consuming task label with building applications which why unsupervised DA do in unlabeled domain. Semi-supervised (SSDA) also small amount Most (UDA) SSDA methods one target. However, it possible exploit multiple domains instead single enhance performance Multi-source (MSDA) more difficult than single-source but efficient. In this research, adapt MDSA evaluate them using sensorial datasets.

Language: Английский

Citations

0