Intelligent Building Automation Research Articles

Green building energy: Patents analysis and analytical hierarchy process evaluation

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance

Lighting designers are always on the quest to develop a lighting control strategy that is aesthetically pleasing, comfortable, and energy-efficient. In an indoor context, electric lighting blended with daylighting controls forms a quintessential component for improving the occupant’s comfort and energy efficiency. Application of soft computing techniques, adaptive predictive control theory, machine learning, HDR photography, and wireless networking have facilitated recent advances in intelligent building automation systems. The evolution and revolution from the 19th to the 21st century in developing daylighting control schemes and their outcomes are investigated. This review summarizes the state-of-the-art artificial intelligence techniques in daylighting controllers to optimize the performance of conventional photosensor-based control and camera-based control in commercial buildings. The past, current, and future trends are investigated and analyzed to determine the key factors influencing the controller design. This article intends to serve as a comprehensive literature review that would aid in creating promising new concepts in daylighting controllers.

Design and Research of Intelligent Building and Electrical Automation

Design and Research of Intelligent Building and Electrical Automation

Application and Design of Intelligent Building Automation Control System

Intelligent building is a safe, comfortable, efficient, energy-saving, convenient and flexible modern building designed and built by organically combining high-tech and architectural art. Building automation control system is an important part of intelligent building, which realizes its own development through the development platform of intelligent building. It can improve the quality of building entities and supporting equipment and the quality of building environment in intelligent building to varying degrees. The building automation system in the intelligent building takes the humanized interface as the emphasis, and takes the combination of mechanical and electrical system, fire control system, access control system and parking management system as the method to create a convenient and comfortable living and working environment for building users. This paper mainly studies the building automation control system of intelligent building, and then describes the content of the building automation control system in intelligent building, and at the same time, carries out in-depth research on related issues, hoping to provide reference for researchers in this field.

Improved energy balance calculation of unitary air conditioners via virtual in-situ calibration

Data-driven technologies for intelligent building energy management and automation have been developed under the energy balance state of systems. The datasets required are typically derived using energy and mass balance principles at each system boundary. Hence, the energy balance of systems significantly affects the system and building performances. However, sensor errors in a building energy sensor network can hinder the identification of balance states, thereby causing energy balance calculation errors in operational systems. In this study, the cooling capacity balance errors between the air and refrigerant sides of two unitary air-conditioner systems, caused by measurement errors, are investigated experimentally. Virtual in-situ sensor calibration (VIC) is performed to determine hidden sensor errors that cause the cooling capacity balance error, as well as to improve the balance and the degraded system performance analysis by calibrating the measurement errors. Experiments reveal the extent of cooling capacity balance errors occurring between the air and refrigerant sides under different operational conditions (0.5%–14.8%). VIC reveals erroneous measurements and the manner by which energy balance errors with measurement errors are solved (below 2%). Finally, the system performance analysis of the two test units shows that the VIC-driven calibration datasets improve the accuracy (0.3%–1.8% errors) of the system energy efficiency ratio even for new test cases after VIC is applied. The primary contributions of this study are as follows: (1) VIC is applied to solve energy balance calculation errors; (2) a strategy is proposed to overcome insufficient datasets to ensure energy balance improvements; and (3) calibrated measurements in real systems are validated.

The Architecture of a Real-Time Control System for Heating Energy Management in the Intelligent Building

Very often, constructors and designers of intelligent building and building automation systems have a choice: to create a compact system with a limited configuration and modifying the system’s behavior possibilities or provide a fully configurable solution at the expense of introducing a full SCADA system equipped with an additional knowledge database and inference system equipped with learning capabilities. In the presented work, we show that there is a third solution. Using a multilayer control system composed of programmable FPGAs, small PCs, and cloud computing resources, we can design and implement a fully configurable intelligent control system for the building’s heating. Our solution combines the compactness of the structure and the ease of installation and assembly.

A Smart Home System based on Microcontroller using Android Application and MySQL Database

In recent years, people may disremember to turn off various devices or not be sure open or closed. Predefined scenarios for these people, home comfort and efficiency in the workplace will help a lot. Most people spend time with mobile phones throughout the day. Therefore, the control of smart devices by phones would be an appropriate solution. In this study, it is designed with microcontroller based intelligent building automation system hardware and software to provide energy efficient, security and comfort. The programming of the microcontroller, writing the android application program for the mobile phone and MySQL database software were realized. Microcontroller with GSM shield, it is possible to control the devices connected to the system by an android application automatically and manually by means of the internet connection by the MySQL database on the remote server. It is ensured that all devices are adjusted with a single touch according to predefined scenarios.

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Intelligent building systems: security and facility professionals’ understanding of system threats, vulnerabilities and mitigation practice

Intelligent Buildings or Building Automation and Control Systems (BACS) are becoming common in buildings, driven by the commercial need for functionality, sharing of information, reduced costs and sustainable buildings. The facility manager often has BACS responsibility; however, their focus is generally not on BACS security. Nevertheless, if a BACS-manifested threat is realised, the impact to a building can be significant, through denial, loss or manipulation of the building and its services, resulting in loss of information or occupancy. Therefore, this study garnered a descriptive understanding of security and facility professionals’ knowledge of BACS, including vulnerabilities and mitigation practices. Results indicate that the majority of security and facility professionals hold a general awareness of BACS security issues, although they lacked a robust understanding to meet necessary protection. For instance, understanding of 23 BACS vulnerabilities were found to be equally critical with limited variance. Mitigation strategies were no better, with respondents indicating poor threat diagnosis. In contrast, cybersecurity and technical security professionals such as integrators or security engineering design professionals displayed a robust understanding of BACS vulnerabilities and resulting mitigation strategies. Findings support the need for greater awareness for both security management and facility professionals of BACS vulnerabilities and mitigation strategies.

Digital Audio Broadcasting (DAB)-based demand response for buildings, electric vehicles and prosumers (DAB-DSM)

The main objective of this paper is to present a new and cost-effective Information and Communication Technology (ICT) tool that can lead to efficient energy management in buildings and optimal operation of electricity networks with increased share of Renewable Energy Sources (RES) and Electric Vehicles (EVs). The new ICT infrastructure is based on the Digital Audio Broadcasting (DAB) standard and its interoperability with smart metering technology, Intelligent Transportation Systems (ITS) and Building Automation Systems (BAS). The main idea involves the attachment of a DAB receiver to electric devices (from small household appliances up to EVs and solar systems and other RES). In this paper, the DAB protocol is described, enabling high cyber-physical security. Moreover, the results of addressing a thermostatically-controlled load using DAB-signaling in Switzerland are also presented. The next steps envisioned are i) the validation of the final protocol version and of the DAB receivers for various electric appliances and DR schemes and, ii) demonstration of the new technology in real-life cases through the National DAB broadcaster in Greece.

Raspberry PI 3B + microcomputer as a central control unit in intelligent building automation management systems

This article aims to show the possible savings in electricity costs in smart building installations with the use of new version of Raspberry Pi 3 model B + as the control unit in intelligent building automation systems. It presents a comparison of the consumption of electricity in two units used in the central control systems, i.e. a small Windows-based computer and a Raspberry microcomputer. The power consumption of these units was measured during the rest period and during standard operations in the intelligent installation system. The conducted measurements proved that the use of the new updated version of Raspberry Pi 3 model B + as the central control unit in intelligent building management systems is more economical and energy-saving.

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Intelligent building automation systems can reduce the energy consumption of heating, ventilation and air-conditioning (HVAC) units by sensing the comfort requirements automatically and scheduling the HVAC operations dynamically. Traditional building automation systems rely on fairly inaccurate occupancy sensors and basic predictive control using oversimplified building thermal response models, all of which prevent such systems from reaching their full potential. Such limitations can now be avoided due to the recent developments in embedded system technologies, which provide viable low-cost computing platforms with powerful processors and sizeable memory storage in a small footprint. As a result, building automation systems can now efficiently execute highly sophisticated computational tasks, such as real-time video processing and accurate thermal-response simulations. With this in mind, we designed and implemented an occupancy-predictive HVAC control system in a low-cost yet powerful embedded system (using Raspberry Pi 3) to demonstrate the following key features for building automation: (1) real-time occupancy recognition using video-processing and machine-learning techniques, (2) dynamic analysis and prediction of occupancy patterns, and (3) model predictive control for HVAC operations guided by real-time building thermal response simulations (using an on-board EnergyPlus simulator). We deployed and evaluated our system for providing automatic HVAC control in the large public indoor space of a mosque, thereby achieving significant energy savings.

An Occupancy Based Cyber-Physical System Design for Intelligent Building Automation

Cyber-physical system (CPS) includes the class of Intelligent Building Automation System (IBAS) which increasingly utilizes advanced technologies for long term stability, economy, longevity, and user comfort. However, there are diverse issues associated with wireless interconnection of the sensors, controllers, and power consuming physical end devices. In this paper, a novel architecture of CPS for wireless networked IBAS with priority-based access mechanism is proposed for zones in a large building with dynamically varying occupancy. Priority status of zones based on occupancy is determined using fuzzy inference engine. Nondominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the optimization problem involving conflicting demands of minimizing total energy consumption and maximizing occupant comfort levels in building. An algorithm is proposed for power scheduling in sensor nodes to reduce their energy consumption. Wi-Fi with Elimination-Yield Nonpreemptive Multiple Access (EY-NPMA) scheme is used for assigning priority among nodes for wireless channel access. Controller design techniques are also proposed for ensuring the stability of the closed loop control of IBAS in the presence of packet dropouts due to unreliable network links.

The Application and Development of the Internet of Things in Intelligent Buildings

The Internet of things (IOT) technology leads the third wave of the information industry having broad application prospects in intelligent building. This paper briefly introduces the background and concept of the IOT, the basis of the integration of intelligent building and the IOT. Intelligent building automation system, safety system, fire alarm system and OA system are the main applications of the IOT in intelligent building. We illustrate the disadvantage of traditional intelligent building and propose that the IOT and intelligent building have a good basis for integration. At the end, this paper analyzes the relationship between green building and intelligent building. The ultimate development goal of them is green building and smart city. It will make a huge impact in people's normal production and life in the future.

Large-Scale Intelligent Building Energy-Saving Measures Based on BA System

With the rapid development of the intelligent building in China, Intelligent building energy consumption has become a social and economic development in a strategic issue that can not be ignored in our society. In this paper, we proposed the energy saving measures in central air conditioning and ventilation system and lighting system of large-scale intelligent building building automation (BA) system from four aspects.

Study on Integrating BACnet with IPv6-based Wireless Sensor Networks

The wireless sensor network with BACnet (Building Automation and Control Networks) is the trend of intelligent building and building automation. In order to provide a new integrate solution for BACnet and WSN, An optimization for standard BACnet stack is proposed. In this paper, current technologies are presented. With the utilization of uIPv6 stack, the data communication between BACnet stack and hardware platform of WSN is realized. An evaluation is finally performed to show with evidence that the integrated system model is feasible.

Towards Automation 2.0: A Neurocognitive Model for Environment Recognition, Decision-Making, and Action Execution

The ongoing penetration of building automation by information technology is by far not saturated. Today's systems need not only be reliable and fault tolerant, they also have to regard energy efficiency and flexibility in the overall consumption. Meeting the quality and comfort goals in building automation while at the same time optimizing towards energy, carbon footprint and cost-efficiency requires systems that are able to handle large amounts of information and negotiate system behaviour that resolves conflicting demands--a decision-making process. In the last years, research has started to focus on bionic principles for designing new concepts in this area. The information processing principles of the human mind have turned out to be of particular interest as the mind is capable of processing huge amounts of sensory data and taking adequate decisions for (re-)actions based on these analysed data. In this paper, we discuss how a bionic approach can solve the upcoming problems of energy optimal systems. A recently developed model for environment recognition and decision-making processes, which is based on research findings from different disciplines of brain research is introduced. This model is the foundation for applications in intelligent building automation that have to deal with information from home and office environments. All of these applications have in common that they consist of a combination of communicating nodes and have many, partly contradicting goals.