Building Energy Management Systems and Techniques: Principles, Methods, and Modelling

By Fengji Luo, Gianluca Ranzi and Zhao Yang Dong

Building 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.

• 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

• Language: English
• Publisher: Elsevier Science
• Release date: Feb 21, 2024
• ISBN: 9780323993012

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.

Book preview

Preface

This book provides an introduction to building energy management techniques and the fundamental design schemes of building energy management systems. In a field where continuous research advancement and commercial system development occur, the book covers the latest directions and methodologies that are studied in both academia and industry in building energy management and energy demand side management. Fundamental concepts related to building energy management systems are introduced with reference to and in the context of smart grids, demand response and demand side management, and distributed renewable energy. This will enable the reader to gain a clear understanding of building-side energy systems and the related technologies that drive the emergence and development of building energy management systems.

Advanced topics are introduced to enable the reader to become familiar with the different energy management scenarios and procedures for modern buildings in an automatic and highly renewable-penetrated building environment. For example, different categories of energy management techniques are presented for different building-side energy resources (including battery energy storage systems, plug-in appliances, and HVAC systems) while providing considerations for accounting for the satisfaction and indoor comfort of the building occupants. The basic principles of evolutionary computation are covered and applied to building energy management problems. Other advanced topics deal with the exploitation of building energy management in the context of building-to-grid integration, peer-to-peer energy trading, and microgrid. The book also introduces concepts related to 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 that can contribute to enhancing the energy efficiency of buildings.

The application examples presented throughout the book aim at introducing the key aspects of the algorithms that support different designs of building energy management systems and their possible implementation. The complexity of the examples is kept to a minimum to enable detailed descriptions of the problem variables and the solution process. The proposed algorithms can be further extended to account for more sophisticated model representations of renewable energy sources, energy storage systems, building models, weather conditions, and indoor comfort, as well as to account for a larger number of problem settings, for example, involving a larger number of appliances, building occupants, and buildings. Pseudocodes of the algorithms used in the examples are provided with explanatory notes.

It is expected that, in the coming decades, a growing number of buildings will be equipped with building energy management systems to enhance their operational energy efficiency and that an effective engagement of the renewable energy sources deployed in buildings will significantly contribute to satisfying the energy demand of buildings.

The authors would like to acknowledge the support of their respective institutions, the University of Sydney and Nanyang Technological University, and of the publishing team from Elsevier.

Fengji Luo

Gianluca Ranzi

Zhao Yang Dong

Chapter 1 Introduction

Abstract

The first part of the chapter provides an overview of the structure and operations of building energy management systems (BEMSs). Particular attention is devoted to highlight some of the key developments that have supported the increasing popularity of BEMSs, such as Building Internet-of-Things technology and smart grids. Recent trends of BEMSs’ designs dealing with the energy management of groups of buildings at a precinct and/or community level are also presented to demonstrate their growing applicability and impact in supporting energy-efficient practices. An overview of the benefits of BEMSs is then outlined to recognize their positive impact on buildings’ operations. The chapter terminates with a brief description of the layout of the chapters included in this book.

Keywords

Demand response; Demand side management; Smart buildings; Energy-efficient buildings; Building energy management system

Contents

1.1Introduction to building energy management systems

1.1.1Background

1.1.2Building energy management systems

1.1.3Energy management strategies and algorithms in BEMSs

1.2BEMSs in smart grids

1.2.1Smart grids

1.2.2Grid-interactive building energy management

1.3Benefits of BEMSs

1.4Layout of the book

References

1.1 Introduction to building energy management systems

1.1.1 Background

In the last decades, the building sector has been one of the main consumers of energy worldwide. In 2020, the energy consumption of this sector accounted for 36% of the global energy consumption worldwide and contributed to 37% of the global energy-related carbon dioxide (CO2) emissions [1]. Fig. 1.1 provides an overview of the CO2 emissions produced by the building sector between 1990 and 2019 [2]. The direct and indirect emissions from electricity and commercial heat used in buildings have increased to 10 GtCO2 in 2019, the highest level ever recorded.

Fig. 1.1

Fig. 1.1 CO 2 emissions produced by the building sector worldwide between 1990 and 2019 [2] .

The large energy consumption of the building sector requires a careful evaluation on how energy is produced and consumed in this sector. In recent years, extensive efforts have been placed in this direction worldwide in establishing dedicated strategies, for example, through initiatives such as the net-zero carbon buildings’ commitment [3] or by the World Green Building Council [4]. Governmental agencies have significantly contributed to implementing strategies to reduce energy consumptions in buildings, such as the European guidelines on Nearly Zero-Energy Buildings (NZEBs) and zero-emission buildings [5,6]. The implementations of these strategies require a collaborative approach among different sectors. In the following and throughout this textbook, we consider how building energy management systems can contribute to minimize the energy consumption of buildings, to support the operations of smart grids, and to enhance the wider acceptable of energy renewables while accounting for the comfort and satisfaction of building occupants.

In the first part of the chapter, we provide an overview of the structure and operations of building energy management systems (BEMSs). Particular attention is devoted to highlight some of the key developments that have supported the increasing popularity of BEMSs, such as Building Internet-of-Things (IoT) technology and smart grids. Recent trends of BEMSs’ designs dealing with the energy management of groups of buildings at a precinct and/or community level are also presented to demonstrate their growing applicability and impact in supporting energy-efficient practices. An overview of the benefits of BEMSs is then outlined to recognize their positive impact on buildings’ operations. The chapter terminates with a brief description of the layout of the chapters included in the book.

1.1.2 Building energy management systems

Building energy management systems, also denoted as BEMSs, are centralized computer systems that monitor and control energy-related resources and devices in building systems. The development of BEMSs has been supported by advances taking place in the fields of microelectronics and information and communication technologies (ICTs).

The early BEMSs can be traced back to the 1970s, e.g., [7,8], when they consisted of a computer-based central station and a group of outstations that consisted of boxes and/or cabinets for relays and connections to sensors and actuators. The central station had the ability to perform simple calculations and perform control decisions. These BEMSs were costly and were mostly used for large installations [8]. In the 1980s, microcomputers have been used to support the deployment of BEMSs because of their increasing computational power. In these earlier installations, BEMSs were typically designed for controlling the major energy components in a building, mostly heating, ventilation, and air conditioning (HVAC) systems, lighting systems, and lifts. In recent years, with the wider deployment of IoT technology [9] in buildings, usually denoted as a BIoT, the BIoT infrastructure enabled controllability of a variety of plug-in electrical appliances and equipment that were not typically managed with traditional BEMSs.

Buildings have been traditionally regarded as pure energy consumers. From this viewpoint, a traditional BEMS would manage a building's energy consumption while aiming to maintain comfortable indoor conditions for the building occupants. The growing acceptance of distributed renewable energy sources (typically wind turbines and solar panels) and energy storage systems installed on building sites has enable buildings to be regarded as prosumers, i.e., both consumers and producers of energy. Such a new role (prosumer) has been supported in the strategies and implementations of BEMSs, therefore moving to manage not only a building's energy consumption but also a building's energy production and storage.

With the penetration of renewable energy sources, buildings can now have a significant impact on the operations of external electric power grid. Now they can also generate and feed energy back to the grid. To satisfactorily operate in this two-way arrangement, a building BEMS is now required to interact with the grid and to support the grid's operations while aiming to optimize the building's energy performance.

The above technical developments need intelligent and sophisticated energy management strategies and algorithms to be implemented in BEMSs to manage the various controllable resources and devices efficiently.

1.1.3 Energy management strategies and algorithms in BEMSs

When referring to a BEMS, it is common to denote the set of computer and automation systems deployed in a building as well as the energy management algorithms, the computing hardware, and the sensing, actuation, and communication facilities.

For the purpose of this textbook, and consistently with available literature (e.g., [10–16]), with the term BEMS, we intend to relate to its energy management strategies and algorithms. Driven by the enhanced controllability in the building's operational environment, extensive effort has been placed in recent years in developing sophisticated energy management strategies for enabling adaptive operational optimizations in the management of building energy resources and devices, e.g., HVAC systems, energy storage systems, electric vehicles, renewable energy sources, and plug-in appliances. Many of these strategies rely on machine learning (e.g., [11–13]) and optimization (e.g., [14–16]) techniques to find optimal operation solutions for energy resources and devices installed in buildings while accounting for specific objective requirements. These sophisticated energy management strategies enable buildings to better adapt to the dynamics of their operational environment (such as the intermittency of the on-site renewable energy and the time-varying energy prices). BEMSs have also found wide applicability in smart buildings [17,18].

Current trends in building energy management strategies and algorithms are to consider the management of group of buildings at a precinct and/or community level, e.g., [19–24]. These energy management techniques consider the coupling of the operations between multiple buildings as well as the impact of their operations on the external environment, e.g., the grid. Underpinning this broader implementation, BEMSs’ strategies have been proposed in recent years to enable energy sharing mechanisms among buildings (e.g., [19–22]) and to aggregately control appliances in multiple buildings part of the same community (e.g., [23,24]). These recent advancements enhance the responsibility and role played by BEMSs in supporting energy-efficient implementations at a large scale in the built environment.

1.2 BEMSs in smart grids

1.2.1 Smart grids

The current energy shortage and climate crisis impose urgent actions for transforming power and energy systems in the way these generate, transform, and distribute energy, as well as for minimizing the use of energy. In this context, smart grids have been proposed at the beginning of the century [25,26] and have found a wide acceptance worldwide [27,28] because of their ability in supporting the implementation of sustainable practices. Traditional power grids heavily rely on fossil fuels to serve the energy demand, while smart grids can rely on a widespread integration of clean and renewable energy sources that can also be distributed geographically, e.g., wind energy, solar energy, and wave energy.

In traditional power systems, the power grid aims at generating the power required to serve the energy loads of buildings according to their energy demand. As a result, considerable costs are required to build the power infrastructure for catering for peak demands of the energy loads. Smart grids follow a different approach and support the interaction between energy loads required by buildings (and other entities) and the grid to establish a more efficient energy system that incorporates both the grid and buildings.

1.2.2 Grid-interactive building energy management

An important feature of smart buildings is their capability of communicating with smart grids and of consuming and producing energy while accounting for the requirements of the grid. This approach is usually referred to as the demand response (see Chapter 4 for more details).

BEMSs provide an effective platform for facilitating the interaction between buildings and the grid. In this context, a BEMS acts on behalf of building occupants to perform automatic control actions on the energy resources and devices installed in the building while considering both the operational requirements of the smart grid, and the needs and preferences of the occupants. Supported by the underlying information and communication infrastructure (briefly introduced in Chapters 3 and 4), a BEMS can perform two-way communications with the grid: (i) a BEMS can receive information from the grid and be aware of the grid's operational requirement; and (ii) a BEMS can report real-time building energy performance to the grid (through sensors and devices deployed in the building), therefore enabling the grid to be aware of the building's energy expected consumption and production. In such a framework, it is possible to implement grid-aware energy management strategies in the design of a BEMS and, therefore, contribute toward an enhanced energy-efficient smart grid.

1.3 Benefits of BEMSs

BEMSs bring multiple benefits to their stakeholders (e.g. building occupants, building owners and managers, and the smart grid) that can be summarized as follows:

–minimization of operational energy costs—with an intelligent energy management, it is possible to reduce the overall energy consumption of buildings and, when available, to enable them to exploit dynamic energy tariffs;

–improved comfort and productivity of building occupants—the operations of a BEMS can increase the comfort level for building occupants by monitoring and controlling the indoor conditions such as temperature, air quality, and lighting. A BEMS can automatically operate plug-in appliances and equipment following the occupants’ requirements and inferred preferences. An enhanced comfort level is expected to lead to increased productivity;

–staff savings and reduced maintenance costs—manual operations on energy resources and devices can be replaced by the automatic communications and controls performed by a BEMS. For example, a BEMS could support a real-time monitoring of the operations of the energy resources and devices and promptly identify faults and potential risks of the energy resources and devices. These methodologies are expected to lead to reduced human intervention and reduced maintenance costs; and

–supporting sustainable energy practices—through an optimized management of energy generation, production, and storage, a BEMS can contribute toward a better utilization of on-site renewable energy sources to locally serve the energy demand of a building. With such an approach, it is possible to reduce the dependency of a building on energy generated from fossil fueled plants, to avoid the energy losses due to long-distance energy transmissions from the grid to buildings, and thereby contributing toward sustainable and energy-efficient practices for buildings and grids.

1.4 Layout of the book

The first part of the book provides an overview of the fundamental concepts related to BEMSs that include the energy resources (Chapter 2), information infrastructure (Chapter 3), and power demand response and demand side management (Chapter 4). The second part of the book (Chapters 5–10) is dedicated to the introduction of building power load forecasting, to BEMSs, and to energy management strategies for major energy devices in buildings. Simple application examples are provided to highlight the implementation of different building power load forecasting methods and different BEMSs’ designs. Pseudocodes are presented to clarify the algorithms underpinning these procedures. The third part of the book (Chapters 11–13) covers the interaction among buildings and between buildings and the grid. In the final chapter (Chapter 14), latest trends in BEMSs’ strategies are presented with a specific focus on engaging building occupants into the building energy management loop. An attempt is made throughout the book to keep the complexity of the application examples to a minimum to enable the discussion of how specific variables vary within a small problem domain. The techniques and topics covered in this book are expected to provide a reference to the research and development of BEMSs for energy-efficient buildings.

References

[1]Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector. Nairobi, Kenya: United Nationals Environment Programme; 2021. (Online). Available from: https://globalabc.org/sites/default/files/2021-10/GABC_Buildings-GSR-2021_BOOK.pdf.

[2]Buildings Sector Energy-Related CO2 Emissions in the Sustainable Development Scenario, 2000–2030. Paris, France: International Energy Agency; 2020. (Online). Available from: https://www.iea.org/data-and-statistics/charts/buildings-sector-energy-related-co2-emissions-in-the-sustainable-development-scenario-2000-2030.

[3]Net Zero Carbon Buildings Commitment. London, UK: World Green Building Council; 2021. (Online). Available from: https://www.worldgbc.org/thecommitment.

[4]World Green Building Council, WorldGBC.org. Available from: https://www.worldgbc.org/contact-us (Accessed 7 March 2022).

[5] European Commission. Recommendations on guidelines for the promotion of nearly zero-energy buildings and best practices to ensure that, by 2020, all new buildings are nearly zero-energy buildings. Off. J. Eur. Union. 2016;L208:46–57.

[6]Proposal for a Directive of the European Parliament and of the Council on the Energy Performance of Buildings. Brussels, Belgium: European Commission; 2021, December Rep. COM (2021) 802 final.

[7] Bentley J.P. Principles of Measurement Systems. fourth ed. London, UK: Longman Scientific and Technical; 1988.

[8] Levermore G.J. Building Energy Management Systems: Applications to Low-Energy HVAC and Natural Ventilation Control. first ed. Boca Raton, Florida, USA: CRC Press; 2013.

[9] Greegard S. The Internet of Things. Cambridge, MA, USA: MIT Press; 2015.

[10] Zhao P., Suryanarayanan S., Simoes M.G. An energy management system for building structures using a multi-agent decision-making control methodology. IEEE Trans. Ind. Appl. 2013;49(1):322–330.

[11] Yu L., Qin S., Zhang M., Shen C., Jiang T., Guan X. A review of deep reinforcement learning for smart building energy management. IEEE Internet Things J. 2021;8(15):12046–12063.

[12] Sunny M.R., Kabir M.A., Naheen I.T., Ahad M.T. Residential energy management: a machine learning perspective. In: Proc. 2020 IEEE Green Technologies Conference (GreenTech), Oklahoma City, USA. 2020.

[13] Magoules F., Zhao H.X. Data Mining and Machine Learning in Building Energy Analysis. Hoboken, New Jersey, USA: John Wiley and Sons; 2016.

[14] Yang R., Wang L. Multi-zone building energy management using intelligent control and optimization. Sustain. Cities Soc. 2013;6:16–21.

[15] Gruber J.K., Huerta F., Matatagui P., Prodanovic M. Advanced building energy management based on a two-stage receding horizon optimization. Appl. Energy. 2015;160:194–205.

[16] Wang F., Zhou L., Ren H., Liu X., Talari S., Shafie-khah M., Catalao J.P.S. Multi-objective optimization model of source-load-storage synergetic dispatch for a building energy management system based on TOU price demand response. IEEE Trans. Ind. Appl. 2018;54(2):1017–1028.

[17] Sinopoli J. Smart Building Systems for Architects, Owners and Builders. Amsterdam, Netherlands: Elsevier; 2009.

[18] Bakker R. Smart Buildings: Technology and the Design of the Built Environment. London, UK: RIBA Publishing; 2020.

[19] Zhao Z., Luo F., Zhang C., Ranzi G. A social relationship preference aware peer-to-peer energy market for urban energy prosumers and consumers. IET Renew. Energy Gener. 2021;doi:10.1049/rpg2.12349 early access.

[20] Cui S., Wang Y.W., Xiao J.W. Peer-to-peer energy sharing among smart energy buildings by distributed transaction. IEEE Trans. Smart Grid. 2019;10(6):6491–6501.

[21] Park B.R., Chung M.H., Moon J.W. Becoming a building suitable for participation in peer-to-peer energy trading. Sustain. Cities Soc. 2022;76:1–16.

[22] Cui S., Xiao J.W. Game-based peer-to-peer energy sharing management for a community of energy buildings. Int. J. Electr. Power Energy Syst. 2020;123:1–9.

[23] Barbato A., Capone A., Carello G., Delfanti M., Merlo M., Zaminga A. House energy demand optimization in single and multi-user scenarios. In: Proc. 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium; 2011, October.

[24] Kakran S., Chanana S. Energy scheduling of residential community equipped with smart appliances and rooftop solar. In: Proc. 7th International Conference on Power Systems (ICPS), Pune, India; 2017, December.

[25]Energy Independence and Security Act 2007. Washington DC, USA: U.S. Government Printing Office; 2007. (Online). Available from: https://www.govinfo.gov/content/pkg/BILLS-110hr6enr/pdf/BILLS-110hr6enr.pdf.

[26] Farhangi H. The path of the smart grid. IEEE Power Energy Mag. 2010;8(1):18–28.

[27] Hashmi M., Hanninen S., Maki K. Survey of smart grid concepts, architectures, and technological demonstrations worldwide. In: Proc. of 2011 IEEE PES Conference on Innovative Smart Grid Technologies Latin America (ISGT LA), Medellin, Colombia; 2011, October.

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Chapter 2 Energy sources in building systems

Abstract

This chapter intends to provide a brief overview of the energy sources that are typically deployed in buildings and that will be considered in coming chapters when dealing with the design of building energy management systems. In the first part of the chapter, we present key features related to wind power and outline common typologies of wind turbines together with design considerations related to their implementation in urban environments. This is followed by an overview of the solar energy and how this can be harvested through different technologies available for building applications. In the final part of the chapter, we present key characteristics of energy storage systems that have been gaining popularity in recent years with the wider penetration of renewable energy sources.

Keywords

Distributed renewable energy sources; Energy storage systems; Battery energy storage systems; Wind power; Solar thermal energy; Photovoltaics solar power; Building-applied photovoltaics; Building-integrated photovoltaics

Contents

2.1Introduction

2.2Wind power

2.2.1Introduction to wind turbines

2.2.2Wind power integration in buildings

2.2.3Wind power output model

2.2.4Worked example

2.3Solar energy

2.3.1Overview

2.3.2Solar thermal energy and its applications in buildings

2.3.3Photovoltaic solar power and its applications in buildings

2.3.4PV solar power model

2.3.5Worked example

2.4Energy storage systems

2.4.1Overview

2.4.2BESSs and their application in buildings

References

2.1 Introduction

Energy consumption in buildings has gained significant attention in recent years due to its growing trend produced, among the others, by the effects of climate change and population growth, and by the increasing installations and use of heating, ventilation, and air conditioning (HVAC) systems. On a positive side, this trend has also encouraged the development of new technologies capable of reducing the dependency of energy generation on fossil fuels by promoting, for example, the deployment and high penetration of renewable energy technologies.

In the last couple of decades, distributed renewable energy sources have been gaining popularity in building applications. Distributed renewable energy sources mainly include small wind turbines and photovoltaic (PV) solar panels that capture energy from wind and solar radiation and convert it into electricity. The term distributed intends to reflect the condition that these energy sources are geographically distributed, for example, in different buildings, and that they are not installed at the macro energy supplier side.

Natural sources, such as solar and wind energy, are intermittent and stochastic in nature. For example, the wind speed at a specific site can frequently vary over time, or the solar radiation can be highly intermittent due to the movements of the sun and clouds. As a result, the power output from a distributed renewable energy resource is highly variable and does not necessarily match the power demand of an energy consumer, such as a building or household. At times, the renewable power output is not sufficient for covering the power demand, while, at other times, the renewable power output is larger. The latter scenario provides opportunities for storing the surplus renewable energy for later use or for sharing it. Energy storage is usually implemented by relying on energy storage systems (ESSs). ESSs can provide energy backup support to buildings in power outage events. With the growing integration of distributed renewable energy sources and ESSs, buildings can now perform self-energy supply operations to serve their own energy demand. In this process, they can reduce their dependency on external energy systems, e.g., electric power grids and gas networks, as it is the case for zero-emission buildings [1,2].

This chapter intends to provide a brief overview of the energy sources that are typically deployed in buildings to provide the basis of some of the technologies that will be considered in later chapters when dealing with the design of building energy management systems. In the first part of the chapter, we present some key features related to wind power and outline the common typologies of wind turbines together with considerations related to their design in urban environments. We then present a mathematical model that can be used to simulate the wind power output when evaluating different building energy management strategies. This is followed by an introduction to solar energy and how this can be harvested with different technologies available for buildings. A simple mathematical model for the PV solar power output is also introduced. In the final part of the chapter, we present key features of ESSs that have been gaining popularity in recent years with the wider penetration of renewable energy