Description
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
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
