Intelligent Building Control Systems : A Survey of Modern Building Control and Sensing Strategies.

Intro -- Series Editors' Foreword -- Preface -- Acknowledgements -- Contents -- Contributors -- Abbreviations -- 1 Introduction and Overview -- 1.1 Overview -- 1.2 The Building Control System -- 1.3 HVAC Control -- 1.4 Beyond HVAC: Lighting, Power, and Network -- References -- Part I Building Level Design and Control Architectures -- 2 Architectures and Algorithms for Building Automation---An Industry View -- 2.1 Introduction -- 2.2 Building Automation Systems -- 2.2.1 BAS Overview -- 2.2.2 HVAC Control Infrastructure -- 2.2.3 New and Emerging Trends -- 2.3 Adaptive HVAC Control: A Cloud-Based Solution -- 2.3.1 Rule-Based Methods -- 2.3.2 Model Predictive Control -- 2.3.3 Implementation of Adaptive MPC -- 2.3.4 Validation -- 2.4 Central Plant Optimization: Concepts and Prototype -- 2.4.1 Overview of Supervisory Optimization -- 2.4.2 Prototype Implementation -- 2.4.3 Role of Data Quality -- 2.5 Automated Demand Response: Smart Buildings Meet Smart Grids -- 2.5.1 Background -- 2.5.2 Participation in Retail and Wholesale DR Programs -- 2.5.3 Automated Demand Response and OpenADR -- 2.5.4 AutoDR Control Methods for HVAC Applications -- 2.5.5 Role of Intelligent Buildings -- 2.6 From Research to Practice---Considerations and Challenges -- 2.6.1 Understanding the Context for Applied Research -- 2.6.2 Role of Architecture -- 2.6.3 ``Supply Chain'' for Technology Transfer -- 2.7 Conclusion -- References -- 3 Operating Systems for Small/Medium Commercial Buildings -- 3.1 Introduction and Motivation -- 3.2 Previous Work: Building Operating System (BOS) -- 3.2.1 Tier 1 : Hardware Presentation Layer -- 3.2.2 Tier 2: Service Layer -- 3.2.3 Tier 3: Application Layer -- 3.3 Software Design of a New Operating System for Small--Medium Commercial Buildings -- 3.4 Implementation and Results -- 3.5 Discussion -- 3.6 Conclusion -- References.

Part II The Heating, Ventilation, Air Conditioning (HVAC) System -- 4 HVAC System Modeling and Control: Vapor Compression System Modeling and Control -- 4.1 Introduction -- 4.1.1 System Configurations -- 4.1.2 Literature Surveys -- 4.2 Dynamic Modeling of Heat Exchangers -- 4.2.1 Multi-phase Flow -- 4.2.2 Fixed Control Volume Models -- 4.2.3 Moving Boundary Models -- 4.2.4 Extreme Transient Scenarios -- 4.2.5 Pressure Drop -- 4.2.6 Model Simplifications -- 4.3 Dynamic Modeling of Actuators and Ancillary Components -- 4.3.1 Compressors -- 4.3.2 Expansion Valves -- 4.3.3 Fans and Pumps -- 4.3.4 Pressure Vessels -- 4.3.5 Pipe Losses -- 4.4 Modeling Challenges -- 4.4.1 Time Scale Selection -- 4.4.2 Computation -- 4.4.3 Parametric Sensitivities -- 4.4.4 Model Tuning and Validation -- 4.5 Control of HVAC Systems -- 4.5.1 Nonlinearity in HVAC Components -- 4.5.2 Linear Control Strategies for HVAC Systems -- 4.5.3 Nonlinear Compensation Techniques -- 4.6 Conclusion -- References -- 5 Model Predictive Control of Multi-zone Vapor Compression Systems -- 5.1 Introduction -- 5.2 Multi-zone Vapor Compression System Description -- 5.2.1 ME-VCS Dynamics -- 5.2.2 ME-VCS Constraints -- 5.3 Control Requirements -- 5.3.1 Baseline Controller Requirements -- 5.3.2 Extended Controller Requirements -- 5.3.3 Control Design Strategy -- 5.4 Model Predictive Control for Baseline Requirements -- 5.4.1 State Estimation Using a Kalman Filter -- 5.4.2 Prediction Model Augmentations -- 5.4.3 Optimal Control Problem Formulation -- 5.4.4 Experimental Validation -- 5.5 Model Predictive Control for Extended Requirements -- 5.5.1 Selectively Deactivating Zones with Reconfigurable MPC -- 5.5.2 Optimization Algorithms for Embedded Platforms -- 5.5.3 Reducing Power Consumption for Demand Response -- 5.6 Conclusion -- References -- 6 Multi-zone Temperature Modeling and Control.

6.1 Motivation -- 6.2 Review of Modeling Approaches -- 6.3 RC Network Thermal Modeling -- 6.4 RC Network Modeling of Building Dynamics -- 6.4.1 Building Element Models -- 6.4.2 Zone Models -- 6.4.3 Multi-zone Modeling -- 6.5 Generic Graph-Based Modeling Framework -- 6.6 Graph-Based Modeling of HVAC Equipment -- 6.6.1 Forced-Air Systems -- 6.6.2 Pumped-Liquid Systems -- 6.6.3 Vapor Compression Systems -- 6.6.4 A Unified Framework -- 6.7 Utility of RC Network and Graph Models -- 6.7.1 Model Reduction -- 6.7.2 Control Architecture Selection -- 6.7.3 Control Strategies -- 6.8 Conclusions and Future Work -- References -- 7 Distributed Model Predictive Control for Forced-Air Systems -- 7.1 Introduction -- 7.2 Forced Air Systems -- 7.2.1 HVAC System Components -- 7.3 Building Automation Systems -- 7.3.1 Control Platforms -- 7.3.2 Communication Systems -- 7.4 Current Building Control Practices -- 7.4.1 ``Trim and Respond'' Logic -- 7.5 Model Predictive Control -- 7.5.1 Cost Design -- 7.5.2 Constraints -- 7.6 Distributed Model Predictive Control for Forced Air Systems -- 7.6.1 One-Step DMPC -- 7.6.2 Comparison of ``Trim and Respond'' and One-Step DMPC -- 7.6.3 Advanced Distributed Model Predictive Control -- 7.7 Conclusion -- References -- 8 Human-in-the-Loop Thermal Management for Smart Buildings -- 8.1 Introduction -- 8.1.1 Existing Solutions Incorporating Occupant Feedback -- 8.2 Human-in-the-Loop System Model -- 8.2.1 Occupant Discomfort Modeling -- 8.2.2 Optimization Objective -- 8.2.3 Building Heat Transfer Model -- 8.3 Singular Perturbation Method -- 8.3.1 Solution Approach -- 8.3.2 System Analysis -- 8.4 Consensus Algorithms -- 8.4.1 Solution Approach -- 8.4.2 System Analysis -- 8.5 Simulation Study -- 8.5.1 Testbed Layout -- 8.5.2 Simulation Results -- 8.6 Concluding Remarks -- References.

Part III Beyond HVAC: Lighting, Grid, and Distributed Intelligence -- 9 Smart Lighting Control Systems -- 9.1 Smart Lighting Systems -- A Brief Survey -- 9.1.1 Control Objectives for Lighting -- 9.1.2 Control Architectures for Lighting -- 9.1.3 Control Methodologies for Lighting -- 9.1.4 Actuation and Sensing Techniques for Lighting -- 9.1.5 Recent Surveys on Lighting Control Systems -- 9.2 The Smart Conference Room -- A Testbed for Lighting Control -- 9.3 Light Transport Modeling -- 9.4 Decentralized Integral Control -- 9.5 Illumination Balancing Algorithm -- 9.5.1 Problem Statement -- 9.5.2 Solution Approach -- 9.5.3 Results -- 9.6 Daylight and Occupancy Adaptive Lighting Control -- 9.6.1 Problem Formulation -- 9.6.2 Solution Approach -- 9.6.3 Results -- 9.7 Spectral Optimization for Polychromatic Lighting -- 9.7.1 Problem Formulation -- 9.7.2 Solution Approach -- 9.7.3 Results -- 9.8 Hierarchical Optimization for Multi-channel LEDs -- 9.8.1 Problem Formulation -- 9.8.2 Solution Approach -- 9.8.3 Results -- 9.9 Effect of Ambient Daylight: Day-Long Experiments -- 9.10 Summary and Conclusion -- References -- 10 Energy Management Systems for Intelligent Buildings in Smart Grids -- 10.1 Introduction -- 10.2 Control of HVAC Systems via Scenario-Based Implicit and Explicit MPC -- 10.2.1 Review of Control Strategies for HVAC Systems -- 10.2.2 HVAC System Modeling -- 10.2.3 SMPC Problem Formulation for HVAC Systems -- 10.2.4 Implicit SMPC Formulation for HVAC Control -- 10.2.5 Explicit SMPC Formulation for HVAC Systems -- 10.3 Building Generation-Side and Demand-Side Management -- 10.3.1 MPC-Based EMS for Smart Buildings -- 10.3.2 Modeling User Behavior Uncertainty -- 10.3.3 A Distributed Approach for Coordinating Shiftable Loads and Storage Systems -- 10.4 Smart Building Demonstrators -- 10.4.1 KTH Live-In Lab -- 10.4.2 Manchester Corridor.

10.4.3 SICS ICE -- 10.5 Coordinating Data Centers and Buildings for a More Effective Load Management -- 10.5.1 Data Centers as Smart Buildings -- 10.6 Conclusions and Future Directions -- References -- 11 Controlling the Internet of Things -- from Energy Saving to Fast Evacuation in Smart Buildings -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Problem Formulation -- 11.3.1 Heterogeneous Information Fusion for Occupants Level Estimation -- 11.3.2 Energy Management for Smart Microgrid of Buildings -- 11.3.3 Building Mounted Wind Turbines for Electric Vehicle Charging -- 11.4 Main Results -- 11.4.1 Results of Heterogeneous Information Fusion -- 11.4.2 Results of Smart Microgrid of Buildings -- 11.4.3 Results of Electrical Vehicle Charging from Wind Turbines -- 11.4.4 Fast Evacuation -- 11.5 Conclusion -- References -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.