Machine Learning and Data Analysis for Energy Efficiency in Buildings

Intelligent Operation, Maintenance, and Optimization of Building Energy Systems
Erscheinungsjahr 2025
ISBN: 978-0-443-28953-8
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm

Machine Learning and Data Analysis for Energy Efficiency in Buildings: Intelligent Operation, Maintenance, and Optimization of Building Energy Systems is a guidebook for big data use in energy efficiency and control. This book begins with an introduction to data basics, from selecting and evaluating data to the identification and repair of abnormalities. In Part II, data mining is covered and applied to energy forecasting, including long- and short-term predictions, and the introduction of occupant-focused behaviour analysis. Part III breaks down the current methods for supply and demand applications, including a variety of solutions for monitoring and managing energy use and supply. Case studies are included in each part to assisting in evaluation and implementation of these techniques across building energy systems. Working from the fundamentals of big data analysis to a complete method for building energy assessment, flexibility, and management, ‘Machine Learning and Data Analysis for Energy Efficiency in Buildings’ will provide students, researchers, and professionals with an essential cutting-edge resource in this important technology.

Autoren/Hrsg.

Zhao, Tianyi

Zhang, Chengyu

Jiang, Ben

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze

Inhaltsverzeichnis

Part I: Data Basics
1. Introduction
2. Data Preparation
3. Abnormal Data Identification and Repair
4. Classification and Definition of Data Type
5. Identification and Repair of Abnormal Energy Consumption Data
6. Case Studies in Different Buildings

Part II: Data Mining
7. Energy Consumption Forecasting
8. Short-time-scale Energy Consumption Prediction (for O&M Regulation)
9. Long-time-scale Energy Consumption Prediction (for Design Evaluation)
10. Case Studies in Different Scenarios

Part III: Data Application
11. Review of Evaluation and Methods for Energy Supply and Demand Matching
12. Energy Supply and Demand Matching Evaluation Methods: Power-load Matching Coefficient
13. Optimization of Supply-side Energy Schemes
14. Optimization of Demand-side Energy Use Solutions
15. Conclusions

Über Autor(innen)

Zhao, Tianyi
Zhao Tianyi is the Deputy Dean and an Associate Professor of the School of Civil Engineering at Dalian University of Technology. He is the Group Lead of the On-line Automation Solutions Institute for Sustainability in Energy and Buildings (OASIS-EB). This group focuses on investigating intelligent regulation and control methods for building energy systems, incorporating advanced technologies such as the Internet of Things, big data, and artificial intelligence. He has published over 100 peer-reviewed articles in journals.

Jiang, Ben
Ben Jiang is a PhD Candidate at the Dalian University of Technology and a member of the Online Automation Solutions Institute for Sustainability in Energy and Buildings, China, led by Professor Zhao. His research focuses on building intelligence applications, including the prediction and analysis of building energy consumption and related parameters.

Zhang, Chengyu
Zhang Chengyu is a PhD student at the Institute for Building Energy and member of the Online Automation Solutions Institute for Sustainability in Energy and Buildings, both at the Dalian University of Technology, China. His main research focus is on energy application for sustainable intelligent buildings, with particular emphasis on energy consumption prediction and anomaly detection and repair of energy monitoring data. One of his most significant contributions in academia is the development of a novel model for building occupant energy-use behavior, which has been integrated into energy consumption prediction to enhance its effectiveness. Additionally, he has collaborated with colleagues to propose strategies for building energy conservation based on adjusting energy-use behaviors and has put forward a comprehensive approach for detecting and repairing anomalies in energy monitoring data.