Intelligent Building Energy Management System Research Articles

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Contextual Rule-Based System for Brightness Energy Management in Buildings

The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

The Application of IoT Technology in Energy Management of Intelligent Building

At present, the construction energy consumption accounts for 30% of the total energy consumption of the city, and this data also has the tendency to rise, therefore, the building energy saving becomes the object that the relevant scholar focuses on. There are many ways of building energy saving, is one of the most critical to building the scientific management of energy, and in this paper, the research content is the application of Internet of things technology of intelligent building energy management, the purpose is to provide a good reference for energy saving. Building energy management system is the basis of precise metering, energy consumption is the key management scheme design, the core is the analysis of the measurement data, based on energy consumption measuring, management solution and to study the way of accessing data processing platform, analyses the power component measurement, clarification of water supply and drainage, HVAC itemized metering and renewable energy metering, designed by computer room air conditioning monitoring andBASenergy management solutions of solar photovoltaic power generation system, it is concluded that theEMSintegration of overall scheme, and based on this, advances the scheme ofBASand the integration ofEMS. The implementation of the scheme effectively, in order to make the management of intelligent building energy management system is also energy consumption data of Internet access was studied, in order to lay a foundation for the realization of the Internet of things technology.

Data-Driven Edge Computing: A Fabric for Intelligent Building Energy Management Systems

Building energy management systems (BEMSs) have been successfully adopted as key control units for modern structures to maintain energy efficiency and provide a comfortable thermal environment for occupants. Recent advances in information and communication technology toward “Industry 4.0” are enhancing the utility of BEMSs. However, challenges, such as how to process the exponentially growing amount of heterogeneous data generated in buildings, need to be addressed to realize “Building 4.0,” which encompasses next-generation smart systems that provide user-centric services. In this article, we propose BEMS–Edge, a framework that integrates seamless, real-time information acquisition, transmission, interpretation, and action in intelligent BEMSs. The primary components, including the Internet of Things (IoT), cloud/edge computing, big data analytics, and artificial intelligence (AI), converge to create a data-driven edge computing fabric offering a range of benefits, such as real-time data analytics and cost savings. The effectiveness of BEMS–Edge is verified by an established, real-world BEMS testbed.

Intelligent energy optimization system development and validation for German building types

Abstract Cost-effective building energy optimization solutions have a strategic function to assist German and European energy policies for the simple reason that buildings account for 40% of the total energy consumption in Europe. The International Energy Agency considers energy efficiency as the first fuel and a major resource. In this work, a cost-effective building energy optimization system consisting of 14 different wireless network embedded sensors, control units (actuators), wireless communication protocol, multi-dimensional information management system, context sensitive graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms is examined. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony (Germany) in the frame of Innovation Support Program between the years 2014 and 2018. The ‘Energy Optimization System (EOS)’, which is subject to this research, reduces the total energy use and CO2 emissions of buildings through optimization of energy-consuming building systems, such as heating, cooling, lighting systems and home/office appliances with the support of an integrated system solution consisting of artificial intelligence-based software and wireless network embedded hardware. The system is deployed and validated in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The developed system can be deployed to all types of historical and new buildings to optimize energy consumption and reduce carbon emissions.

Economic, social and environmental impact analysis of an indigenously developed energy optimization system for German office and residential building types

Abstract This work addresses the economic, social and environmental analysis of an indigenously developed building energy optimization system for residential and office buildings in Germany. The developed system consists of 14 different wireless network embedded sensors, wireless communication protocol, multi-dimensional data warehouse, graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony—Germany in the frame of Innovation Support Program between the years 2014 and 2018. The system is tested in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The analysis is accomplished within the framework of four main indicators. These are economic indicators including calculations of lifetime cost and payback rate methods, resource use indicators including the calculation of reduced energy use as a result of the energy efficiency provided by the system, social indicator including additional expendable income calculations and environmental indicators including calculations depicting the benefits of reducing harmful emissions. Analysis results illustrate that building energy optimization systems provide a cost-effective method for promoting energy efficiency goals. Results of the indicators prove monetary benefits for the building occupants in terms of resource use, return on investment and social impact, as well as quantifiable benefits for easing the harmful effects of climate change phenomenon.

Lightweight KPABE Architecture Enabled in Mesh Networked Resource-Constrained IoT Devices

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications.

Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation

The common approach to model occupants behaviors in buildings is deterministic and consists of assumptions based on predefined fixed schedules or rules. In contrast with the deterministic models, stochastic and agent based (AB) models are the most powerful and suitable methods for modeling complex systems as the human behavior. In this paper, a co-simulation architecture is proposed with the aim of modeling the occupant behavior in buildings by a stochastic-AB approach and implementing an intelligent Building Energy Management System (BEMS). In particular, optimized control logics are designed for smart passive cooling by controlling natural ventilation and solar shading systems to guarantee the thermal comfort conditions and maintain energy performance. Moreover, the effects of occupant actions on indoor thermal comfort are also taken into account. This study shows how the integration of automation systems and passive techniques increases the potentialities of passive cooling in buildings, integrating or replacing the conventional efficiency strategies.

Design of a dynamic key management plan for intelligent building energy management system based on wireless sensor network and blockchain technology

In modern buildings, the intelligent building energy management system (IBEMS) faces several problems with its centralized architecture: the difficulty in the networking between end devices, the lack of flexibility, and the limited sharing of underlying information. To overcome these problems, this paper probes into the framework of the wireless sensor network (WSN), and designed a network model of the IBEMS. Next, the security of blockchain technology was fully examined, and a dynamic key management strategy was proposed based on the blockchain for the IBEMS. The feasibility of the proposed plan was verified through experiments. The experimental results show that the proposed plan reduces the data storage time and space of each sensor, and optimizes the control of the IBEMS. The research results provide a reference for setting up a safe and reliable IBEMS based on spatial distribution, and help promote blockchain technology in other scenarios of the UPIoT.

LED lighting systems for smart buildings: a review

This study presents a review of smart light-emitting diode (LED) lighting systems applied to smart buildings. The study is focused on drivers, protocols, technologies, communication networks and applications. An extended overview of the methodologies used for LED lighting control in smart buildings is addressed. The study also presents an integrated architecture able to achieve the necessary services and control methodologies for intelligent building energy management system for LED lightings systems in smart buildings.

Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

Intelligent context-awareness system for energy efficiency in smart building based on ontology

Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user's comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption.

Using Intelligent Building Energy Management System for the Integration of Several Systems to one Overall Monitoring and Management System

In the CommONEnergy project the interoperation and optimization of active systems in shopping malls is performed by using an intelligent Building Energy Management System (iBEMS) which integrates open communication protocols for speaking with several industrial systems. The iBEMS assures the communication of several systems using open communication protocols in order to transfer the required information while on the same time provides a Human to Machine Interface (HMI) for the control and monitoring of the connected systems. In parallel the iBEMS stores historical values for future comparison of the performance. With the operation of the iBEMS the energy manager of a building can overview in real time the operation of all the systems’ components and take decisions which can assist with the proper operation. In parallel the programming routines of the iBEMS adjust automatically the operation of the systems using inputs from the installed sensors and from external data (e.g. weather forecast).

Research and Realization of Intelligent Building Energy Management System

Research and Realization of Intelligent Building Energy Management System

Micro-climatization and real-time digitalization effects on energy efficiency based on user behavior

An approach to intelligent building energy management systems at the workplace level has the potential to save energy through the use of building information modeling (BIM) and smart sensor systems. Its development focuses on micro-climatization and real-time digitalization systems from an architect's point of view. Intelligent management systems (IMSs) have significant potential for energy savings, but they have not been fully used in buildings and cities. Smart sensor systems based on user behavior will improve indoor environmental quality (IEQ) and user comfort. A cost-effective strategy has been implemented by using a low-technology and high-performance approach. This work aims to reduce energy consumption and provide comfort conditions by learning user behavior. Furthermore, it seeks to create a real-time system with regard to the functions of developed technologies. In order to improve energy efficiency and comfort conditions, smart sensor systems and digital simulation tools play a crucial role in finding optimal solutions to optimization problems.

On Defining Information and Communication Technology Requirements and Associated Challenges for ‘Energy and Comfort Active’ Buildings

The intention of this article is to highlight considerations and ensuing challenges encountered in attempts to define communication requirements for the proposed multi-agent based building energy management systems. This is within the framework of ‘TKI-Smart Grid BEMS’ project which aims at developing new generation intelligent Building Energy Management Systems having capacity to interact with the utility power systems distribution network. The article identifies the development of comfort and energy active buildings as key to deriving maximum benefits from electrical smart grids for the built environment. These buildings require well specified information and communication technology for operational success. The paper is based on critical literature review. This is followed by a discussion on the challenges associated with specifying ICT infrastructure for multi-agents systems-based energy and comfort active buildings.

Research and Design of Intelligent Building Energy Management System

In the field of intelligent building, managing energy consumption effectively in various types of construction equipment, improving energy efficiency, has been seen as an important and difficult subject. This thesis through establish building energy management system; realize building energy management by unified management of construction equipment.

Intelligent building energy management system using rule sets

The current study presents an intelligent decision support model using rule sets based on a typical building energy management system. In addition, the model's impact on the energy consumption and indoor quality of a typical office building in Greece is presented. The model can control how the building operational data deviates from the settings as well as carry out diagnosis of internal conditions and optimize building's energy operation. In this context, the integrated “decision support model” can contribute to the management of the daily energy operations of a typical building, related to the energy consumption, by incorporating the following requirements in the best possible way: (a) the guarantee of the desirable levels of living quality in all building's rooms and (b) the necessity for energy savings.

LOCAL OPERATING NETWORKS TECHNOLOGY AIMING TO IMPROVE BUILDING ENERGY MANAGEMENT SYSTEM PERFORMANCE SATISFYING THE USERS PREFERENCES

The available Building Energy Management Systems (BEMS), although they contribute to a significant reduction of energy consumption and improvement of the indoor environment, they can only be implemented in new buildings. Their installation in existing buildings is far from being cost effective due to the incompatibility of communication protocols between BEMS designed by various manufacturers and unavoidable modifications for data transmission. On the other hand, current research for energy efficient buildings has proved that although the design and the facilities including BEMS aim to satisfy the thermal and visual comfort plus the air quality demands while minimising the energy needs, they often do not reach their goals due to users interference. Latest trends in designing Intelligent Building Energy Management Systems (IBEMS) offer a Man Machine Interface that could store the users preferences and adapt the control strategy accordingly. The objectives of the present paper are to present the advantages of the use of a man machine interface based on a smart card terminal together with fuzzy control techniques in satisfying the users preferences plus to underline the capabilities that the LON network offers to the design. A fuzzy PID controller is developed to reach the first of the above objectives. The monitoring of the energy consumption along with satisfying the users preferences is achieved by the use of a suitable cost function for the whole system. All the above parameters as well as the cost function are kept between acceptable limits. The overall control system including the cost function is modeled and tested using MATLAB/SIMULINK. The implementation of the control system in an existing building requires interconnection of sensors and actuators installed across the building, is well served by the LonWorks technology due to its high standards and flexibility features.