Designing Smart Building Management Systems for Sustainable and Cost-Efficient Housing

The growing demand for sustainable and cost-efficient housing solutions has spurred the development of Smart Building Management Systems (SBMS), which leverage advanced technologies such as Internet of Things (IoT) devices, artificial intelligence (AI), and data analytics to optimize building performance. This paper explores the design, implementation, and impact of SBMS on promoting energy efficiency, resource management, and overall cost reduction in housing. The study highlights key design principles, including the integration of smart technologies, the use of sustainable materials, and the optimization of control systems like HVAC, lighting, and waste management. Additionally, it examines common challenges in SBMS implementation, such as technological, financial, and regulatory barriers, and proposes solutions like innovative financing models, public-private partnerships, and updated regulatory frameworks to facilitate broader adoption. The paper also identifies gaps in existing research, such as the need for more studies on technology integration, scalability, and user engagement. Finally, the paper concludes by offering recommendations for future research, focusing on the development of emerging technologies, policy frameworks, and user behavior studies to enhance the efficacy of SBMS in achieving long-term sustainability and cost-efficiency in housing.

Authors

Publication Details

Journal
International Journal of Advanced Multidisciplinary Research and Studies
Published
2024-12-31
DOI
https://doi.org/10.62225/2583049x.2024.4.6.3994
Citations
8
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
2.56
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article

Designing Smart Building Management Systems for Sustainable and Cost-Efficient Housing

Tochi Chimaobi Ohakawa, Abiodun Benedict Adeyemi, Rasheed O Ajirotutu, Fasasi Lanre Erinjogunola et al.
8 citations
International Journal of Advanced Multidisciplinary Research and Studies
Smart Cities and Technologies
2.56
article

Designing Smart Building Management Systems for Sustainable and Cost-Efficient Housing

Tochi Chimaobi Ohakawa, Abiodun Benedict Adeyemi, Rasheed O Ajirotutu, Fasasi Lanre Erinjogunola, Saliu Alani Adio, Tosin Samuel Oyetunji
article en
8 citations

Abstract

The growing demand for sustainable and cost-efficient housing solutions has spurred the development of Smart Building Management Systems (SBMS), which leverage advanced technologies such as Internet of Things (IoT) devices, artificial intelligence (AI), and data analytics to optimize building performance. This paper explores the design, implementation, and impact of SBMS on promoting energy efficiency, resource management, and overall cost reduction in housing. The study highlights key design principles, including the integration of smart technologies, the use of sustainable materials, and the optimization of control systems like HVAC, lighting, and waste management. Additionally, it examines common challenges in SBMS implementation, such as technological, financial, and regulatory barriers, and proposes solutions like innovative financing models, public-private partnerships, and updated regulatory frameworks to facilitate broader adoption. The paper also identifies gaps in existing research, such as the need for more studies on technology integration, scalability, and user engagement. Finally, the paper concludes by offering recommendations for future research, focusing on the development of emerging technologies, policy frameworks, and user behavior studies to enhance the efficacy of SBMS in achieving long-term sustainability and cost-efficiency in housing.

International Journal of Advanced Multidisciplinary Research and StudiesVol. 4(6)
Openalex Percentile: Top 9%
Smart Cities and Technologies
2.56
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Designing Smart Building Management Systems for Sustainable and Cost-Efficient Housing — Tochi Chimaobi Ohakawa, Abiodun Benedict Adeyemi, et al. · International Journal of Advanced Multidisciplinary Research and Studies (2024) | TGRS Research Map | TGRS