Building Automation Systems Research Articles

Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption

Green building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machine learning to minimize energy consumption and improve indoor sustainability. The dataset is utilized to predict cooling and heating individually, with data visualization by graphically illustrating dataset features and preprocessing through Z-Score normalization and dataset splitting. The proposed model, based on active learning and utilizing ML regressors such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN), and Logistic Regressor (LR), shows significant performance improvements. The CBR-AL model achieves impressive results with values of 0.9975 for cooling (Y1) and 0.9883 for heating (Y2), indicating a high level of accuracy. The model’s success in reducing energy consumption and improving sustainability has potential ripple effects, including substantial cost savings, reduced carbon footprints, and improved operational efficiency in green buildings. This approach not only enhances environmental sustainability but also sets a benchmark for future advancements in predictive modelling for energy management.

Connected smart elevator systems for smart power and time saving

Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns through advanced encryption and access control mechanisms will be essential for safeguarding sensitive data in smart elevator systems.

Building Management Systems in residential buildings: their role in energy and indoor climate resilience

Building Management Systems in residential buildings: their role in energy and indoor climate resilience

Integration of Legacy Industrial Equipment in a Building-Management System Industry 5.0 Scenario

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost.

Development of an intelligent building management systembased on BIM technology

The construction of smart cities has led to new demands for intelligent building management. This paper proposes an innovative building management system that integrates BIM, IoT, cloud computing, big data, and artificial intelligence technologies. The system architecture is rational, and its functional modules are comprehensive, achieving breakthroughs in key areas such as BIM visualization, fault diagnosis, and energy consumption optimization. Through practical testing, the system demonstrates outstanding capabilities in data processing, real-time performance, and fault recognition accuracy, providing solid technical support for the operation of green intelligent buildings and contributing to sustainable urban development.

Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency

Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. Data on energy usage, weather conditions, occupancy trends, and building features is collected and prepared to start the process. GBM model attributes are created using sequential relationships and initial load projections using LSTM networks. Combining LSTM with GBM takes advantage of each model's strengths: LSTM's sequential data processing and GBM's complex nonlinear connection capture. Performance measures like RMSE and MAE are used to evaluate the hybrid model's validity. Compared to individual models, the integrated LSTM-GBM method improves prediction accuracy. This higher predictive capacity allows real-time energy management systems, improving building operations and reducing energy use. Implementing this integrated model in Building Management Systems (BMS) shows its practicality in achieving sustainable building energy efficiency.

Solar photovoltaic buildings: The combination of sustainable energy and green buildings

Solar photovoltaic (SPV) buildings are at the cutting edge of renewable energy technology, offering a promising solution to meet our growing energy needs while minimizing environmental impact. This comprehensive review explores the multifaceted realm of solar PV buildings, examining their design, construction, energy performance compared to traditional structures, environmental consequences, and future possibilities. SPV buildings hold significant potential in promoting sustainability and achieving energy self-sufficiency. These structures not only generate clean energy but also contribute surplus electricity to the grid, enhancing community resilience and reducing dependence on traditional energy sources. Design and construction innovation involves implementing advanced architectural designs, utilizing smart building management systems, and incorporating renewable materials. These practices not only improve energy efficiency but also enhance the overall aesthetic appeal of structures. Performance evaluations consistently show that SPV buildings outperform traditional structures in terms of energy efficiency, carbon footprint reduction, and long-term cost-effectiveness. This translates to reduced energy consumption and environmental burdens. Environment issues such as energy-intensive panel production and waste disposal necessitate ongoing research to mitigate any negative environmental impact, thus necessitating technological developments to minimize these ramifications. SPV buildings represent the future of energy-efficient construction, leading the way to a cleaner and more eco-friendly world. Through tireless research and innovation, these structures may soon become mainstream solutions, providing environmental preservation as well as energy independence benefits for society at large.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

Leveraging Cost-Effective AI and Smart Technologies for Rapid Infrastructural Development in USA

High cost of building makes houses expensive for US citizens and residents. Thus, this study proposes the leveraging of cost-effective artificial intelligence (AI) and smart technologies (ST) for rapid infrastructural development in US. It considers them as sustainable means of tackling the challenges for the attainment of affordable houses. The study explores the potentials of prominent AI and smart technologies capable of reducing the cost of building houses in the US, for which houses would become affordable for all. The primary data are obtained from telephone interviews with 10 construction workers and 5 experts of AI, alongside observation and introspection. The secondary data are drawn from library and the internet. Qualitative method, thematic and content analyses, systematic review, and descriptive and interpretive tools are employed. The results show Machine Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Robotic Process Automation to be prominent cost-effective AI technologies, while Building Automation Systems, Internet of Things, Renewable Energy Systems, and Smart Water Management Systems are cost-effective smart technologies. The study concludes that the identified AI and smart technologies are not only cost-effective, but also transformative and innovation-driven and can be leveraged to increase efficiency, productivity, quality delivery and satisfactory services. The study recommends them to government and organizations for cost-effectiveness towards attaining rapid infrastructural development in the USA.

Resilient energy management strategy in smart residential buildings considering price attack: An aggregative game perspective

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated.

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems.

Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants' needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS's underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research.

The Role of Internet of Things (IoT) in Transforming Facilities Management in Smart Cities

The shift in urban foundation and administrations inside keen cities requests a comprehensive, innovative, and user-centric strategy that continues coordinating cutting-edge advances. This paper presents a spearheading approach that leverages the control of the Web of Things (IoT), Counterfeit Insights (AI), and Machine Learning (ML) to revolutionize the way shrewd city offices are overseen. At the centre of this strategy is the improvement of a centralized, cloud-based Offices Administration Stage (FMP) that serves as the spine for keen city operations. The FMP acts as an integrated hub, drawing together information from numerous IoT sensors, building automation systems, and other urban infrastructure. The policy also encourages user-centred design in light of improving occupant comfort, productivity, and well-being. Backed by IoT sensors and AI-driven building computerization frameworks, robust natural condition control and vitality utilization streamlining of the FMP can assist in conveying a predominant client involvement inside savvy city office.

Integrating Building Management Systems in High-Rise Structures using Building Information Modeling

Rapid industrialization and population growth in urban areas has resulted in scarce land resources thus changing builtform tremendously. Complex construction such as high-rise structures are becoming an essential requirement in urbanareas. Efficient Building Management System (BMS) such as fire safety, heating ventilation and air-conditioning,security, and surveillance along with the operation and maintenance is crucial for high-rise construction projects. Theconstruction industry is one of the most labor-intensive industries in the world with a human centric managementapproach. With the advent of computer technologies, planning and management of building systems in a constructionproject have become easier. The concept of Building Information Modeling (BIM) to manage building managementsystems in construction projects is gaining popularity. In this study, an integrated framework is proposed that includesnumerous aspects of building management systems in high-rise structures with a dynamic BIM model. This proposedframework was explored with a case illustration of a multi-story building. Preliminary results of the model arepresented herewith.

Best practices in cybersecurity for green building management systems: Protecting sustainable infrastructure from cyber threats

This study explores the critical intersection of cybersecurity and sustainable infrastructure, with a focus on Green Building Management Systems (GBMS). Recognizing the increasing sophistication of cyber threats and the integration of digital technologies in sustainable buildings, this research aims to understand the challenges and prospects of cybersecurity within this context. Employing a systematic literature review and content analysis, the study examines peer-reviewed articles, conference proceedings, and industry reports from 2010 to 2024. The methodology facilitates a comprehensive understanding of the evolution, current practices, and future directions of cybersecurity measures in sustainable infrastructure. Key findings reveal that robust cybersecurity measures are foundational to protecting the digital and physical assets underpinning sustainable infrastructure. The study identifies core principles of cybersecurity, such as resilience and the integration of cybersecurity with sustainability efforts, as crucial for enhancing the security posture of GBMS. Looking ahead, the research anticipates a future where cybersecurity measures are seamlessly integrated into sustainable buildings, ensuring resilience against cyber threats while advancing sustainability goals. Strategic recommendations include adopting international standards, fostering interdisciplinary collaboration, investing in cybersecurity education, and leveraging emerging technologies. The study concludes that advancing research in cybersecurity technologies tailored for sustainable infrastructure is essential for navigating the complexities of cybersecurity in green building management.

Transition towards Positive Energy District: a case study from Latvia

The transition from a fossil fuel-based energy system to renewable energy sources has become a crucial consideration in both national-scale planning processes and local-scale energy system planning. Research has been conducted to seek technical solutions for a specific district of a university campus located on the peninsula of Riga to implement a smart energy system with multiple energy sources, storage systems, and energy efficiency measures. The solutions for the transformation of Energy Island to an Energy Community include power generation by building integrated solar panels with different power storage alternatives, such as thermal storage, batteries, hydrogen, and fuel cells. The simulation also evaluates the potential for heat recovery and waste heat integration from the data center and cooling systems to cover the heat demand. The interlinkage with energy efficiency improvements through improved building management systems provides an opportunity to increase RES utilization rates and improve overall energy efficiency. To provide a holistic overview of the future development of the urban area, the research also compares the connection to the district heating network. Results show that the analyzed energy community can achieve up to 80% of the RES self-consumption level as cost-optimal solutions by combining off-site wind turbine, solar PV panels, water source heat pumps, waste heat recovery and adjusted power storage.

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Optimal layout scheme of fire detectors and operation condition monitoring technology in urban integrated substation

Aiming at the problem that it is difficult to obtain the life cycle law and detection coverage of fire detectors and the low reliability of fire alarm process in urban integrated substation, an optimal layout scheme and operation condition monitoring technology of fire detectors based on local outlier factor and neural network are proposed. Divide fire zones by equipment functions, calculate the number of fire detectors required by using ground area and indoor height, and integrate building automation system to create substation fire alarm system; The signal noise of fire detector equipment is eliminated by wavelet transform, the fuzzy characteristic analysis model of fire detector operation state is established, the local outlier factor is simulated as abnormal data, the past operation data is trained by BP(Back propagation) neural network, the difference between the current state and the normal operation state is obtained, and the detector operation state monitoring is completed. The operation reliability of fire detector is analyzed from the aspects of temperature and smoke perception. The simulation results show that the proposed technology can successfully obtain the life cycle law and detection coverage of fire detector, and ensure the safe use of urban integrated substation. In addition, through a series of experiments, the feasibility and effectiveness of this technology in practical applications are verified.The maximum time consumption of detector working condition monitoring in this method is only 3.3 s. The fitting degree of the method in this paper is 5.7 ml/m3, and the fitting degree of the actual smoke curve is 5.7 ml/m3. The temperature of the method in this paper is lower than 33 °C. In the future development, the introduction of more advanced self-diagnosis technology can be real-time monitoring and analysis of the status of the detector itself, timely detection of faults or anomalies, and automatic alarm or notification of maintenance personnel for repair and maintenance.

A Numerical Analysis Based Internet of Things (IOT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings

The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building’s technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors.

Applying OPTICS with and without PCA for fault detection of fan coil units using building automation system data

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA’s dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data.