Intelligent energy optimization system development and validation for German building types

Gökçe, Hasan Ufuk; Gökçe, Kamil Umut

doi:10.1093/ijlct/ctab049

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…BEMSs play a crucial role in optimizing energy consumption and enhancing building efficiency, particularly in smart and sustainable cities [19].…”

Section: Building Energy Management Systems (Bemss)mentioning

confidence: 99%

Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration

Kozlovska,

Petkanic,

Vranay

et al. 2023

Energies

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This paper presents a comprehensive analysis of the potential benefits and feasibility of integrating Building Energy Management Systems (BEMSs) with Building Information Modeling (BIM) in, but not limited to, the construction and building management sectors. By examining advantages, challenges, and real-world case studies, this study offers valuable insights into the impact of BEMS-BIM integration on building operations. The research methodology includes a literature review and bibliometric analysis to understand the subject domain and identify prevalent keywords. Additionally, case studies demonstrate the effectiveness of BEMS-BIM integration in real-world scenarios. This study investigates the possibilities and challenges of BIM to the BEMS methodology for energy-efficient industrial buildings, emphasizing the importance of addressing uncertainties and enhancing software interoperability. This research highlights the potential of BEMS-BIM integration to revolutionize building performance, enhance sustainability, and contribute to a greener and more efficient future for the construction and building management industries.

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“…BEMSs play a crucial role in optimizing energy consumption and enhancing building efficiency, particularly in smart and sustainable cities [19].…”

Section: Building Energy Management Systems (Bemss)mentioning

confidence: 99%

Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration

Kozlovska,

Petkanic,

Vranay

et al. 2023

Energies

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Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review

Li,

Antwi-Afari,

Anwer

et al. 2024

Buildings

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Artificial intelligence (AI) has emerged as an effective solution to alleviate excessive carbon emissions in sustainable building projects. Although there are numerous applications of AI, there is no state-of-the-art review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. Therefore, this review study aims to conduct a systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects, thereby expediting the realization of NZCEs in building projects. A mixed-method approach (i.e., systematic literature review and science mapping) consisting of four comprehensive stages was used to retrieve relevant published articles from the Scopus database. A total of 154 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. Six mainstream research topics were identified and discussed. These include (1) life cycle assessment and carbon footprint, (2) practical applications of AI technology, (3) multi-objective optimization, (4) energy management and energy efficiency, (5) carbon emissions from buildings, and (6) decision support systems and sustainability. In addition, this review suggests six research gaps and develops a framework depicting future research directions. The findings contribute to advancing AI applications in reducing carbon emissions in sustainable building projects and can help researchers and practitioners to realize its economic and environmental benefits.

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Emergence of AI—Impact on Building Condition Index (BCI)

West,

Siddhpura,

Evangelista

et al. 2024

Buildings

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The Building Condition Index (BCI) is a widely adopted quantitative metric for assessing various aspects of a building’s condition, as it facilitates decision-making regarding maintenance, capital improvements and, most importantly, the identification of investment risk. In practice, longitudinal BCI scores are typically used to identify maintenance liabilities and trends and proactively provide indications when maintenance strategies need to be altered. This allows for a more efficient resource allocation and helps maximise the lifespan and functionality of buildings and their assets. Given the historical ambiguity concerns because of the reliance on visual inspections, this research investigates how AI and using ANN, DNN and CNN can improve the predictive accuracy of determining a recognisable Building Condition Index. It demonstrates how ANN and DNN perform over asset classes (apartment complexes, education and commercial buildings). The results suggest that DNN architecture is adept at dealing with diverse and complex datasets, thus enabling a more versatile BCI prediction model over various building categories. It is envisaged that with the expansion and maturity of ANN, DNN and CNN, the BCI calculation methodologies will become more sophisticated, automated and integrated with traditional assessment approaches.

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Intelligent energy optimization system development and validation for German building types

Cited by 3 publications

References 11 publications

Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration

Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration

Artificial Intelligence in Net-Zero Carbon Emissions for Sustainable Building Projects: A Systematic Literature and Science Mapping Review

Emergence of AI—Impact on Building Condition Index (BCI)

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