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Building Energy Management Systems and Techniques
Principles, Methods, and Modelling
- 1st Edition - February 21, 2024
- Authors: Fengji Luo, Gianluca Ranzi, Zhao Yang Dong
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 6 1 0 7 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 3 0 1 - 2
Building Energy Management Systems and Techniques: Principles, Methods, and Modelling presents basic concepts, methodologies, modeling techniques, and fundamental design schemes of… Read more
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Request a sales quoteBuilding Energy Management Systems and Techniques: Principles, Methods, and Modelling presents basic concepts, methodologies, modeling techniques, and fundamental design schemes of building energy management systems. Covering the latest developments and methodologies from academia and industry, the book brings together energy management, demand response, evolutionary computation, and fundamental programming.
The authors explore the basic concepts related to building energy management systems and put them into the context of smart grids, demand response and demand-side management, internet of things, and distributed renewable energy. Advanced topics provide the reader with an understanding of various energy management scenarios and procedures for modern buildings in an automatic and highly renewable-penetrated building environment. This includes a range of energy management techniques for building-side energy resources such as battery energy storage systems, plug-in appliances, and HVAC systems. The fundamental principles of evolutionary computation are covered and applied to building energy management problems. The authors also introduce the paradigm of occupant-to-grid integration and its implementation through personalized recommendation technology to guide the occupants’ choices on energy-related products and their energy usage behaviors, as well as to enhance the energy efficiency of buildings. The book includes several application examples throughout, illustrating for the reader the key aspects involved in the implementation of building energy management schemes.
Building Energy Management Systems and Techniques is an invaluable resource for undergraduate and postgraduate students enrolled in courses related to energy-efficient building systems and smart grids and researchers working in the fields of smart grids, smart buildings/homes, and energy demand response. The book will be of use to professional electrical, civil, computing, and communications engineers, architects, and building energy consultants.
The authors explore the basic concepts related to building energy management systems and put them into the context of smart grids, demand response and demand-side management, internet of things, and distributed renewable energy. Advanced topics provide the reader with an understanding of various energy management scenarios and procedures for modern buildings in an automatic and highly renewable-penetrated building environment. This includes a range of energy management techniques for building-side energy resources such as battery energy storage systems, plug-in appliances, and HVAC systems. The fundamental principles of evolutionary computation are covered and applied to building energy management problems. The authors also introduce the paradigm of occupant-to-grid integration and its implementation through personalized recommendation technology to guide the occupants’ choices on energy-related products and their energy usage behaviors, as well as to enhance the energy efficiency of buildings. The book includes several application examples throughout, illustrating for the reader the key aspects involved in the implementation of building energy management schemes.
Building Energy Management Systems and Techniques is an invaluable resource for undergraduate and postgraduate students enrolled in courses related to energy-efficient building systems and smart grids and researchers working in the fields of smart grids, smart buildings/homes, and energy demand response. The book will be of use to professional electrical, civil, computing, and communications engineers, architects, and building energy consultants.
- Integrates the latest techniques in the building energy management paradigm, such as appliance scheduling, peer-to-peer energy trading, and occupant-to-grid integration
- Provides extensive application examples to help readers understand the design principles of different building energy management systems
- Includes step-by-step guidance on the methods, modeling techniques, and applications presented in the book, including evolutionary computations
- Provides pseudocodes and optimization algorithms for the application examples to enable the reader to gain insight into the modeling details
Postgraduate and advanced undergraduates enrolled in courses related to energy-efficient building systems and smart grids, and researchers working in the fields of smart grid, smart building/home, and energy demand response, Electrical engineers, civil engineers, building energy consultants, computing and communication engineers
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter 1 Introduction
- Abstract
- 1.1 Introduction to building energy management systems
- 1.2 BEMSs in smart grids
- 1.3 Benefits of BEMSs
- 1.4 Layout of the book
- References
- Chapter 2 Energy sources in building systems
- Abstract
- 2.1 Introduction
- 2.2 Wind power
- 2.3 Solar energy
- 2.4 Energy storage systems
- References
- Chapter 3 Information infrastructure for building energy management
- Abstract
- 3.1 Introduction
- 3.2 Building automation systems
- 3.3 Building Internet-of-Things
- 3.4 Cloud computing
- 3.5 An integrated information infrastructure for BEMSs
- References
- Chapter 4 Power demand response and demand side management
- Abstract
- 4.1 Introduction
- 4.2 Basic concepts
- 4.3 Power systems
- 4.4 Smart grids
- 4.5 Demand response and demand side management
- 4.6 Demand response for grid peak power reduction
- 4.7 Incentive-based demand side management
- 4.8 Price-based demand side management
- 4.9 Application example
- 4.10 Comparison between incentive- and price-based DSM strategies
- References
- Chapter 5 Building energy management systems
- Abstract
- 5.1 Introduction
- 5.2 Overview of a building energy management system and its operations
- 5.3 Typical energy resources managed by BEMSs
- 5.4 Main functional features of BEMSs
- References
- Chapter 6 Building power load forecasting
- Abstract
- 6.1 Introduction
- 6.2 Explanatory and time series STLF models
- 6.3 Exponential smoothing-based STLF
- 6.4 ARIMA-based STLF
- 6.5 ANN-based STLF
- 6.6 Introduction to deep neural network-based STLF
- References
- Chapter 7 Evolutionary optimization
- Abstract
- 7.1 Introduction
- 7.2 Illustrative optimization example
- 7.3 Introduction to evolutionary algorithms
- 7.4 Genetic algorithm
- 7.5 Particle swarm optimization
- 7.6 Differential evolution algorithm
- 7.7 Control parameters of evolutionary algorithms
- 7.8 Evolutionary algorithms applied to benchmark functions
- 7.9 Remarks for evolutionary optimization
- References
- Chapter 8 Energy management of building-integrated battery energy storage systems
- Abstract
- 8.1 Introduction
- 8.2 Types of BESSs in building applications
- 8.3 Model of a BESS
- 8.4 BESS integration for emergency power backup for buildings
- 8.5 BESS integration for time-varying energy tariff
- 8.6 BESS integration with on-site renewable energy
- 8.7 Optimization-based BESS energy management
- 8.8 Introduction to vehicle-to-building/home integration
- References
- Chapter 9 Energy management of flexible electric appliances
- Abstract
- 9.1 Introduction
- 9.2 Modeling time shiftable appliances
- 9.3 Modeling power adjustable appliances
- 9.4 A simple energy management scheme for TSAs
- 9.5 A simple energy management schemes for PAAs
- 9.6 Energy management for TSAs: An optimization-based approach
- 9.7 Energy management scheme for both TSAs and PAAs with renewable energy penetration: An optimization-based approach
- References
- Chapter 10 Energy management of HVAC systems
- Abstract
- 10.1 Introduction
- 10.2 Introduction to HVAC systems
- 10.3 Thermal model for the modeling of indoor conditions
- 10.4 Model representation for the operation of an HVAC
- 10.5 Evaluation of the HVAC's energy consumption and energy cost
- 10.6 Energy management of HVACs with varying set-temperatures
- 10.7 Pre-heating/cooling-based HVAC energy management
- 10.8 HVAC energy management for renewable energy accommodation
- References
- Chapter 11 Energy sharing among buildings
- Abstract
- 11.1 Introduction
- 11.2 An internal energy-sharing pricing scheme for buildings
- 11.3 Peer-to-peer energy trading among buildings
- 11.4 Blockchain-enabled P2P energy trading
- References
- Chapter 12 Building-to-grid integration
- Abstract
- 12.1 Introduction
- 12.2 From direct load control to building-to-grid integration
- 12.3 Building energy management for peak-to-average ratio optimization
- 12.4 A building-to-grid system for emergency load shedding
- References
- Chapter 13 Buildings and microgrids
- Abstract
- 13.1 Introduction
- 13.2 Introduction to microgrids
- 13.3 Buildings in a microgrid
- 13.4 Operating a building as an off-grid microgrid during power outages
- References
- Chapter 14 Occupant-to-grid integration
- Abstract
- 14.1 Introduction of occupant-to-grid integration
- 14.2 Introduction to personalized recommendation technology
- 14.3 Application example: Personalized building-side renewable investment recommendation system
- 14.4 Other applications of personalized recommendation in occupant-side energy systems
- References
- Index
- No. of pages: 332
- Language: English
- Edition: 1
- Published: February 21, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780323961073
- eBook ISBN: 9780323993012
FL
Fengji Luo
Fengji Luo is an Academic Fellow and a Lecturer in the School of Civil Engineering at The University of Sydney. His research interests include power demand response and demand side management, building/home energy management, smart grid, and computational intelligence and its applications in smart grids and smart buildings.
Affiliations and expertise
Academic Fellow and Researcher, School of Civil Engineering, The University of Sydney, AustraliaGR
Gianluca Ranzi
Gianluca Ranzi is a Professor in the School of Civil Engineering and Director of the Centre for Advanced Structural Engineering at The University of Sydney. His research interests fall in the fields of structural engineering, architectural science, building energy management and power demand side management, and heritage conservation.
Affiliations and expertise
Professor and Director, Centre for Advanced Structural Engineering, School of Civil Engineering, The University of Sydney, NSW, AustraliaZD
Zhao Yang Dong
Zhao Yang Dong is a Professor in the School of Electrical and Electronics Engineering at
Nanyang Technological University, Singapore. His previous roles include Director of UNSW
Digital Grid Futures Institute, Ausgrid Chair Professor and Director of Ausgrid Centre for Intelligent Electricity Networks led R&D support for the Smart Grid, Smart City national demonstration project in Australia. He is a Fellow of IEEE for his contributions in computational methods in power system planning and stability. His research interests include smart grid, power demand response and demand side management, energy market and economics, and power system stability and control.
Affiliations and expertise
Professor, School of Electrical and Electronics Engineering, Nanyang Technological University, SingaporeRead Building Energy Management Systems and Techniques on ScienceDirect