Artificial Intelligence in Building Performance Analytics

Erscheinungsjahr 2026
ISBN: 978-981-9240-49-4
Verlag: Springer Verlag, Singapore

Buch, Englisch, 375 Seiten, Format (B × H): 155 mm x 235 mm

This book (1) integrates artificial intelligence into modeling, evaluation, and optimization of building systems, enabling multi-scale and multi-objective analysis; (2) uncovers the underlying mechanisms and interdependencies influencing building energy, comfort, and carbon performance across spatial and temporal dimensions; (3) presents cutting-edge analytical and optimization methods that support data-informed design, diagnosis, and operation; and (4) demonstrates the applicability and scalability of AI techniques through representative case studies and methodological examples. By uniting theoretical advancement with practical implementation, this book offers both a scientific foundation and a reference guide for researchers, engineers, and policymakers striving to enhance the intelligence, efficiency, and sustainability of the built environment.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Limao

Lin, Penghui

Zhang, Yan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieeffizienz
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Bauingenieurwesen Bauökologie, Baubiologie, Bauphysik, Bauchemie
• Geisteswissenschaften Architektur Ökologische Aspekte in der Architektur
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

Introduction to Building Performance Analytics.- Green Motivation Exploration.- Green Certification Valuation.- Spatio-Temporal Heterogeneity Analysis.- Driving Force Analysis.- Building Energy Consumption Estimation.- Green Building Evaluation and Diagnosis.- Building Energy Consumption Forecast.- Anomalies Detection in Energy Consumption.- Building Energy Consumption Mitigation.- Energy-Efficient Building Design.- Building Green Retrofit Planning.- Optimization-Based HVAC Control.- Conclusions and Future Directions.

Über Autor(innen)

Dr. Limao Zhang is currently a professor at the School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, China. Prior to his appointment at HUST, he served as an assistant professor at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. He serves as an editor-in-chief of Journal of Building Design and Environment and editorial board member of Automation in Construction. His research focuses on Construction Automation, Risk Analysis, and Urban Resilience.

Dr. Yan Zhang is currently a research scientist at the College of Computing and Data Science (CCDS), Alibaba–NTU Global e-Sustainability Lab (ANGEL), Nanyang Technological University, Singapore. Her research centers on AI-driven sustainability and urban building energy systems, including smart sustainable buildings, ESG analytics, and large language models (LLMs). 

Dr. Penghui Lin is currently a research fellow at the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore. He obtained his Doctor’s Degree from Nanyang Technology University in 2025 and his Master’s degree from National University of Singapore in 2019. Dr. Lin’s principal research area involves smart construction and building energy analysis. Dr. Lin has rich research experience and participated in many collaborative projects with industrial partners. Dr. Lin is a reviewer for many international journals, such as Automation in Construction and Energy.