Machine Learning and Data Analysis for Energy Efficiency in Buildings

Intelligent Operation, Maintenance, and Optimization of Building Energy Systems

Tianyi Zhao, Chengyu Zhang, Ben Jiang (Autoren)

Buch | Softcover

428 Seiten

2025
Elsevier - Health Sciences Division (Verlag)
9780443289538 (ISBN)

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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.

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.

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

Erscheinungsdatum	27.09.2025
Reihe/Serie	Advances in Intelligent Energy Systems
Verlagsort	Philadelphia
Sprache	englisch
Maße	152 x 229 mm
Gewicht	450 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik ► Elektrotechnik / Energietechnik
ISBN-13	9780443289538 / 9780443289538
Zustand	Neuware