Machine Learning and Data Analysis for Energy Efficiency in Buildings: Intelligent Operation, Maintenance, and Optimization of Building Energy Systems

by Tianyi Zhao, Chengyu Zhang, Ben Jiang

Overview

Machine Learning and Data Analysis for Energy Efficiency in Buildings: Intelligent Operation, Maintenance, and Optimization of Building Energy Systems introduces data basics, from selecting and evaluating data to the identification and repair of abnormalities. Other sections cover data mining applied to energy forecasting, including long- and short-term predictions, the introduction of occupant-focused behavior analysis, and current methods for supply and demand applications. Case studies are included in each part to assist 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, this book provides students, researchers, and professionals with an essential, cutting-edge resource on this important technology. - Builds from data basics to complex solutions and applications for energy efficiency in building systems - Includes step-by-step methods for data anomaly and fault identification, repair, and maintenance - Provides real-world case studies and applications for immediate use in research and industry

Product Details

ISBN-13:	9780443289545
Publisher:	Elsevier Science
Publication date:	09/26/2025
Series:	Advances in Intelligent Energy Systems
Sold by:	Barnes & Noble
Format:	eBook
Pages:	250
File size:	17 MB
Note:	This product may take a few minutes to download.

About the Author

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.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.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.

Table of Contents

Part I: Data Basics1. Introduction2. Data Preparation3. Abnormal Data Identification and Repair4. Classification and Definition of Data Type5. Identification and Repair of Abnormal Energy Consumption Data6. Case Studies in Different BuildingsPart II: Data Mining7. Energy Consumption Forecasting8. 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 ScenariosPart III: Data Application11. Review of Evaluation and Methods for Energy Supply and Demand Matching12. Energy Supply and Demand Matching Evaluation Methods: Power-load Matching Coefficient13. Optimization of Supply-side Energy Schemes14. Optimization of Demand-side Energy Use Solutions15. Conclusions

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From the Publisher

Guides readers through methods of selecting, monitoring, and repairing big data for system management of energy-efficient buildings