Building Automation Systems Research Articles

Predicting uncertainty of a chiller plant power consumption using quantile random forest: A commercial building case study

Selecting an appropriate confidence level for chillers models could lead to better prediction of the peak electricity load, to more efficiently participate in demand response programs, and to manage the operation and performance of chiller plants. Therefore, this study aims to predict the power consumption of a chiller plant in a high-rise commercial building based on Quantile Random Forest (QRF). This study proposed a simplified data-driven approach to compute the lower and upper bounds of the prediction intervals (PIs) of power consumption for a chiller plant, consisting of four chillers. An evaluation of the proposed data-driven approach using measured datasets and computed PIs metrics demonstrates that the PI coverage probability for the samples in test datasets for all chiller models with the confidence level of 80% and 90%, are above 80% and 90%, respectively. Also, the results show that by increasing the confidence level from 80% to 90%, the upper and lower bounds of the demand charge differ from the actual value by a factor of 3.3 and 1.7 times greater, respectively. This approach has implications for developing sequences of operations for chiller plants in building automation systems for evaluating past performance and for achieving future optimization of these plants.

Analyzing Energy Efficient Design Strategies in High-rise Buildings with Reference to HVAC System

The Global Warming, Climate Change, Energy Depletion, and Carbon emissions are the greatest risks to humanity in the 21st century. Urbanization in many ways increases the lack of energy resources and demands more construction of high-rise towers for a better standard of living. Buildings are responsible for around one-third of global greenhouse gas emissions. With the emergence of the energy crisis, the rise of environmental consciousness, energy-saving measures, and sustainable buildings became a topic of attention. Due to advancements in technology, the design of high-rise buildings has come across various changes with large glass openings, sealed windows, and deep plans which increase the dependency on air conditioning systems (HVAC) thus increasing the demand for more energy consumption inside the building due to mechanical ventilation and air conditioning. In this paper, an attempt has been made to understand the strategies used for designing energy efficient high-rise buildings that can lower the heating and cooling load and thus increase the performance of the high-rise building. The research is based on a theoretical approach and qualitative analysis which is identified from the literature review and secondary case studies, Case studies selected for the study are office high-rise buildings which are sustainable skyscrapers and have achieved lower energy consumption in heating and cooling load compared to the typical office building which shows how an increase in natural ventilation and other architectural design parameters such as building envelop, form, orientation and integration technology like Building Management System (BMS) system can make it more energy efficient high rise building.

A Study on the Green Building Trend in China—From 2001 to 2022, Focusing on Research Topic Words

The Chinese government has committed to achieving carbon neutrality by 2060. Consequently, a shift towards green building development in the construction industry is inevitable. The purpose of this study is to summarize and predict the development direction of China’s green buildings by understanding their changing trends over time. Accordingly, this study employed content analysis as the research method to analyze 69 research papers and their topic words published from 2001 to 2022, with further in-depth analysis from four sub-dimensions: technical, economic, policy, and systemic. Overall, since 2000, China’s green building research has mainly focused on energy-saving technology and economic aspects, with particular attention being paid to the role of incentive policies. Research on the energy-saving renovation of existing buildings is insufficient, and research on building energy-saving management systems has been scarce. This may be related to China’s emphasis on technology and its neglect of management functions such as operations and maintenance. Considering the important role of management in the construction industry, it is crucial for the academic community to strengthen research on a comprehensive and systematic green building management system and for the Chinese government to increase its support for green building management and practices.

The impact of improved PCA method based on anomaly detection on chiller sensor fault detection

With the widespread use of building automation systems (BAS), a large amount of chiller operating data is often readily available, which provides a good basis for optimizing the control of unit sensors. However, these data are highly coupled and contain a large number of outliers which would significantly affect sensor fault detection. Compared with the traditional PCA method, the improved kernel PCA can effectively handle nonlinear data. Meanwhile, anomaly detection has shown excellent ability in rejecting outliers, so an anomaly detection step before fault detection is also necessary. Therefore, to address the problem of data presence coupling and outliers, this study attempts to investigate the impact of different anomaly detection methods on PCA and KPCA based chiller sensor fault detection. Based on the optimal combination of features screened by importance and correlation analysis to construct a fault detection model, the impact of two anomaly detection methods (Isolation Forest and K-means) on KPCA fault detection was analyzed and compared. To obtain general results, an experimental dataset of RP-1043 is used to validate the study. The results show that both anomaly detection methods can improve the overall quality of the original training data. Compared with the traditional PCA method, KPCA improves the detection efficiency by 1.7%–28.29% at all fault magnitudes. The IF-KPCA and Kmeans-KPCA improve the fault detection efficiency by 3.66%–14.63% and 3.17%-20% on top of KPCA. This study would provide reference for the optimal feature combination selection and anomaly detection method selection for practical engineering.

Challenges for Latvian Apartment Building Management Companies Regarding with Performance of their Services on the Way to Maintain Sustainable Housing Fund

Abstract The legislation of the Republic of Latvia in the field of apartment building management are not enough effective both for apartment owners and apartment building management companies due to complicated decision-making procedure, which slows down the management process, and as consequences are clients‘ unsatisfaction with apartment management service. Within this research authors studied theoretical aspects of service quality, studied apartment building management system in Latvia and compared it with Finland. Finland was chosen due it is located geographically close to Latvia, and Latvia is closer to the Scandinavian culture and lifestyle. Authors made survey of apartment building owners and made interview with expert to find out the current problems of apartment building management services. A monographic and descriptive method was used to analyze the theoretical aspects of service quality, secondary data analysis and expert interview were used to describe the situation of industry of apartment building management services, but quantitative research method such as survey of the clients of the apartment building owners, in connection with the comparative, analytical, deductive, logical approach method was used in this research.

Interoperable, platform independent structural health monitoring with long‐term availability

AbstractWithin the FFG‐project PreMainSHM the area of preventive structural health monitoring is raised to a new level of networked systems. This applies not only to the networking of self‐sustaining sensors and their sensor data, but also to the networking and utilization of relevant information for the building condition assessment and building management. The paper presents and discusses the basic considerations in terms of data models and interfaces of cloud‐based systems to guarantee interoperability between different software systems and long‐term‐availability and accessibility not only of raw but also of and relevant meta and analysed data. The starting point for further developments is provided by the existing technologies of the involved partners, which is a GIS based infrastructure asset management system and wireless as well as fibre optic sensor systems from the authors. In order to obtain information relevant to the building management, suitable analysis and prognosis procedures are discussed. Data models and analysis tools are developed and integrated into an interoperable software framework in order to provide relevant status information that can be either assessed directly by web user interfaces in form of a georeferenced digital twin or that can be used in building management systems.

Energy-oriented control retrofit for existing HVAC system adopting data-driven MPC – Methodology, implementation and field test

Energy conservation and carbon reduction of existing buildings have been receiving increasing attention with the proposed carbon neutrality goals. As presented in many studies, adopting model predictive control (MPC) for building HVAC system control is an effective means to realize building energy conservation. However, existing studies rarely addressed the critical challenges for practical applications and integration of MPC in existing Building Automation Systems (BASs). This study proposes a control retrofit approach, which is lightweight and replicable, to realize the improvement of energy efficiency of the existing building HVAC systems through integrating data-driven MPC into existing BASs. The critical challenges in the practical applications of MPC strategies, including MPC strategy development, large-scale data processing, strategy deployment and integration with existing BASs are addressed. The proposed approach was applied to the existing HVAC system of an actual airport terminal and the evaluation results indicate that the control retrofit approach is effective in achieving energy efficiency and thermal comfort improvement. The system daily energy consumption was reduced by 24.5% in average and the percentage of the discomfort time was reduced from 70.2% to 5.7% in average after the control retrofit. The proposed control retrofit approach, with the help of the scalability and distributed computing capabilities of the cloud computing platform, is expected to be able to realize lightweight control retrofit of large number of existing building HVAC systems.

Demonstrating a smart controller in a hospital integrated energy system

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid.

Modelling the WELL building concepts for office environments: PLS-SEM approach

Purpose The health, well-being and productivity (i.e. WELL) of office building occupants are vulnerable to poor office environments. Therefore, this study aims to identify new features and concepts of office buildings in supporting occupants’ WELL. To achieve that aim, this study: explores new WELL features for office buildings, develops new WELL concepts for office buildings and examines the influence of the newly developed WELL concepts on existing WELL building standard (WELL v2) concepts. Design/methodology/approach The first phase involved ten experts to assign weightage for health, well-being and productivity. In the second phase, 206 questionnaire survey data were collected from office building occupants throughout Malaysia. Exploratory factor analysis established new WELL concepts for office buildings. Partial least-squares structural equation modelling examined the influence of the newly developed WELL concepts on the existing WELL v2 concepts. Findings Two new WELL concepts were developed: “space and services”, consisting of sufficient space, workstation privacy, office layouts, building automation systems, cleanliness and information technology (IT) infrastructure, and “building security”, consisting of security systems and safety at parking lots. Here, “space and services” influences all existing WELL v2 concepts, and “building security” influences the water, nourishment, mind and community concepts of WELL v2. Originality/value This study uncovers holistic WELL building concepts to support occupants’ health, well-being and productivity with additional new features and concepts for construction industry policymakers to establish holistic building assessment tools.

Towards democratization of digital twins: Design principles for transformation into a human-building interface

While legacy building digital twins (DTs), such as building management systems, help building and facility managers to make informed decisions, the real end-users of buildings, i.e., occupants, have been considered as data informants without agency for access to and interaction with DT systems. This is due to the lack of multi-user interfaces for occupants to interact with building systems effectively with sufficient observability and controllability. The research on the design of multi-user interfaces has been limited to specialized services in public, shared spaces, or office-specific experiments and fails to produce detailed design principles. In this study, we explore the “democratization of DTs” as an occupant-building interface that invites occupants to monitor the building- and human-related data and make informed decisions. An occupant participatory approach was employed, consisting of a survey, an experience sampling method, and a co-design workshop, to understand occupants' current experiences with environmental controls and desired methods to interact with a DT system. The findings identified five interaction dimensions between occupants and a novel occupant-in-the-loop DT system that provides occupants with observability and controllability over building systems. Accordingly, we establish design principles for DT systems as an occupant-building interface that supports occupants’ dynamic contexts, intuitive controls, collaborative and balanced decision-making between personal comfort and energy consumption, as well as authority and data privacy.

GreEn-ER–Electricity consumption data of a tertiary building

The increased use of intermittent renewable energy sources makes the use of machine learning methods combined with demand-side management more and more frequent. Machine learning algorithms rely on data to identify patterns and learn insights. Hence, data availability is of utmost importance, and the more, the merrier. Therefore, this data report aims to present a dataset concerning the electricity consumption of a tertiary building located in the French Alps region (Grenoble) in 2017 and 2018. It is a massively monitored and controlled building with about 330 electricity meters, whose measurement data constitute the dataset. The data were collected directly from the building management system and correspond to raw data, without any pre-treatment. The dataset also includes Python notebooks that allow for understanding the system design, navigating the data, and performing some simple analyses. This is a publicly available dataset that tries to fill the gap of the availability of electricity consumption data, especially regarding tertiary buildings.

A reinforcing transfer learning approach to predict buildings energy performance

PurposeThe purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.Design/methodology/approachThis paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.FindingsIt was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.Research limitations/implicationsThe case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.Originality/valueThis approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

Using KNX-Based Building Automation and Control Systems for Data Exfiltration

When it comes to protecting confidential and/or sensitive information, organizations have a plethora of recommendations, standards, policies and security controls at their disposal, conceived to deal with a wide variety of threats. However, most of them share the same fundamental premise: that weaknesses are inline by nature, as a consequence of infrastructure, social and/or technological gaps that can be detected, controlled, mitigated or constrained. Side channel threats are a different matter, though. Stemming from unconventional intrusion or attack vectors whose existence was inconceivable, unexpected or deemed unfeasible, their successful exploitation may provide attackers with the means to bypass and render most security controls ineffective or even useless. In this paper we address one such case: the use of a KNX-based building automation and control system to exfiltrate data from an air-gapped infrastructure. The introduction of a small device provides connectivity to the existing KNX fieldbus and enables sending data through it or even control other devices, with no interference in the operation of the building automation and control network. We validated the feasibility of this approach by means of an experimental setup, which was used to successfully evaluate two different techniques: inline bus exfiltration and optical transmission, via dimmer control. Finally, some measures for detecting and mitigating this type of attacks are proposed.

Cyber-physical systems and digital twins for “cognitive building” in the construction industry

Purpose Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions. Design/methodology/approach Scientometric analysis and content analysis were adopted for this study. Findings The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB. Originality/value This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

Advanced Monitoring Techniques for Optimal Control of Building Management Systems for Reducing Energy Use in Public Buildings

Advanced Monitoring Techniques for Optimal Control of Building Management Systems for Reducing Energy Use in Public Buildings

Edge-based solution for battery energy management system: Investigating the integration capability into the building automation system

Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.

DIGITALIZATION OF SUSTAINABLE INTEGRATED PROPERTY MANAGEMENT FOR AFFORDABLE STRATA HOUSING

The main problem faced by the joint management building (JMB) in the management of strata buildings is in the aspect of taking action against residents who refuse to pay the building management fee. The management of JMB itself faces problems in the proper way of managing the building management system and facilities, financial systems and community-based communication systems. In addition to the building management system which is still operated manually, the management of financial management fee collection is still implemented in a separately. Moreover, dissatisfaction of residents in terms of delayed in corrective maintenance management action of strata buildings, correlate to poor fee payment and further put pressure on the sustainable continuity of strata housing management. Digitalization of sustainable integrated property management which includes aspects of financial management, dissemination of communication within the community and safety and security in the management of affordable strata housing needs to be given special attention. This study proposes a framework for digitalization of sustainable integrated property management for affordable strata housing that the system is able to harmonize various aspects of the needs of strata residents to create a strata liveable life. This will then will be realized through the development of web-based system for the purpose of commercialization of sustainable integrated property management system for affordable strata housing that beneficial for the well-being of strata building residents as well as the JMB.

Post-occupancy evaluation for enhancing building performance and automation deployment

Building automation systems are building control and management systems that control/operate different systems in a building such as HVAC, lighting, safety systems, security and access control, surveillance, indoor air quality, and waste management. These systems manage, control, and integrate other building systems to enhance living experience and optimise energy use through a set of sensors and actuators that are deployed in the building to collect data and execute commands that are sent through a gateway. These management systems generate real-time commands and act or reacts to stochastic occupant behaviours. Buildings in Oman used to be built with burned clay mixed with date palm leaves, and wadi stones and date palm trunks are used for building structure. This architecture keeps the indoor environment warm in winter and cold in summer. However, with advancing technology there has been a shift in building towards using concrete and steel that have high thermal conductivity and pose some challenges for energy use. This paper will show that the main concern of occupants in Oman is indoor temperature control, especially during summer months. This has raised concerns for building performance in Oman and shows a need to review building materials, recommend sustainable alternatives, and more widely, implement sustainable building techniques. This paper seeks to develop recommendations to overcome these issues by exploring post-occupancy satisfaction measures and potential use of BAS and sustainable building materials to enhance building performance in Oman. Following on from this, this paper explores the motivations behind people's decision in Oman to deploy home automation and explore techniques to enhance building performance.

Deployment of crowdsourced occupant data to support fault detection and diagnosis in buildings

This proof-of-concept paper proposes a method that integrates occupants' feedback and personal data to enhance the performance of fault detection and diagnostic (FDD) technology in buildings. The proposed approach uses a smartwatch app called Cozie to collect occupants' subjective feedback on thermal comfort and air quality, and the data is transferred to a cloud database through a data stream. Similarly, an application programming interface is used to retrieve sensor readings from the building automation system. FDD rulesets then use the data to determine process variable errors and report alarms. The results of an experiment show that occupant input accurately prove/disproved alarms and allowed for a faster and more accurate FDD. The occupants' feedback also helped prompt rule-based FDD algorithms and support the alarms triggered. Two independent surveys were also conducted. According to the first survey, occupants tend not to report discomfort to building operators through conventional methods unless the indoor temperature is more than 3 °C warmer or cooler than their desired temperature. The proposed method helped occupants report discomfort issues earlier, and the percentage of occupants choosing 3 °C warmer or colder conditions decreased by 29%. The paper concludes that the integration of occupants' feedback and personal data can significantly enhance FDD performance and provide more comfortable environments for building occupants.

Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems

Model-based optimal control has proven its effectiveness in optimizing the performance of central air-conditioning systems in terms of thermal comfort and energy efficiency. It was often assumed that temperature distribution in the entire air-conditioned space is uniform and can be represented by a single or averaged measurement in optimization. However, actual distribution in the air-conditioned space is usually uneven, which can affect thermal comfort and indoor air quality. The dynamics of air conditioning systems and their interactions with the built environment exist in both time and space domains. Conventional data-driven approaches typically concentrate on the temporal correlations only among building operation data, while neglecting the spatial correlations. This study proposed a spatio-temporal data-driven methodology for optimal control of central air conditioning systems, which aims to address the challenging issue of uneven spatial distributions of environmental parameters in air-conditioned space. The methodology consists of graph-based multi-source data integration, graph neural network-based indoor environment modeling and spatio-temporal model-based online optimal control. The proposed methodology is tested on real air handling unit-variable air volume (AHU-VAV) system in a high-rise office building through a cloud-based platform. Spatio-temporal data from building automation system (BAS), internet of things (IoT) devices and building information modeling (BIM) are integrated and organized as graph structure. Graph neural network models are developed for predicting the evolution of indoor air temperature distribution under different control settings. The developed graph neural network-recurrent neural network (GNN-RNN) model architectures show enhanced accuracy than conventional deep learning models (i.e., convolutional neural network-recurrent neural network (CNN-RNN), Dense-RNN). And the cloud-based online test demonstrates that the proposed model-based control strategy improves the percentage of time achieving Grade I thermal comfort from 36.5% (i.e., existing control strategy) to 81.3%.