Building Operation Management Research Articles

Federating cross-domain BIM-based knowledge graph

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology’s efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building’s lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Semantic model-based large-scale deployment of AI-driven building management applications

Digitalization and Artificial Intelligent (AI) are revolutionizing building operation management. The abundance of data generated with the digitalization of buildings in the whole lifecycle can be harnessed to enhance building operational efficiency through data-driven control and optimization applications. However, the heterogeneity of data across building datasets hampers data interactivity and interoperability, presenting obstacles to the large-scale deployment of AI-enabled data-driven solutions. A semantic model-based framework is developed to integrate multi-sources data from buildings' air-conditioning system, supporting the large-scale deployment of AI-enabled data-driven building management applications. Both static and temporal data from multi sources are stored in the database guided by the semantic model. To demonstrate the framework's effectiveness, a building cooling load prediction application is implemented and evaluated across three typical buildings. The successful deployment of the proposed semantic model-based framework demonstrates its potential for facilitating large-scale deployment of AI-enabled data-driven applications in building sector.

A systematic literature review into simulation for building operations management theory: reaching beyond positivism?

ABSTRACT This paper examines the extent to which simulation modelling has been employed beyond positivism, particularly under post-positivist and design-science-oriented paradigms, for theory building in operations management. Our study demonstrates that existing literature reviews have neglected the aspect of theory building and the generation of technological rules from a design-science perspective. A systematic literature review appraises 53 studies, published between 1976 and 2019, revealed that classical positivist approaches predominate (40 of 53 studies), followed by directive positivist (6), classical post-positivist (5), and participatory post-positivist (2) paradigms. The review highlights the potential of simulation modelling for theory building in operations management from both positivist and post-positivist perspectives. However, the dominance of positivist approaches suggests a lack of engagement with practical implementation and comparative studies. The findings advocate for greater external validation and practitioner involvement, and integrated and extended checklists for simulation studies are proposed to guide future research in this area.

AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects

Reducing carbon emissions from buildings is crucial to achieving global carbon neutrality targets. However, the building sector faces various challenges, such as low accuracy in forecasting, lacking effective methods of measurements and accounting in terms of energy consumption and emission reduction. Fortunately, relevant studies demonstrate that artificial intelligence (AI) and big data technologies could significantly increase the accuracy of building energy consumption prediction. The results can be used for building operation management to achieve emission reduction goals. For this, in this article, we overview the existing state-of-the-art methods on AI and big data for building energy conservation and low carbon. The capacity of machine learning technologies in the fields of energy conservation and environmental protection is also highlighted. In addition, we summarize the existing challenges and prospects for reference, e.g., in the future, accurate prediction of building energy consumption and reasonable planning of human behavior in buildings will become promising research directions.

POTENTIAL OPPORTUNITIES OF BLOCKCHAIN TECHNOLOGY IN STREAMLINING THE MALAYSIAN COMMERCIAL OFFICE BUILDING OPERATION MANAGEMENT PROCESS

Managing commercial real estate is extremely complex and cumbersome and involves many stakeholders who provide access to and modify a variety of information. This led to some common issues with traditional commercial real estate management such as lack of transparency, third-party interference, the complexity of the agreements, record keeping and security, and absence of real-time data. Accordingly, blockchain technology is anticipated to address the issues of data openness, trustworthiness, and correctness in the global real estate market. This research aims to discover the potential opportunities that blockchain could offer in streamlining the commercial office building operation management process. A conceptual framework of blockchain-based technology adoption in the current Malaysian commercial office building operation management process was developed based on the literature review and focus group discussion (FGD). The framework was underpinned by four core operation management that emerge from the research findings including financial, administrative, technical, and promotional management.

Ten questions concerning occupant-centric control and operations

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the IEA-EBC Annex 79 research program's subtask on real world implementations of OCC during the past 5 years. First, in Q1 and Q2, we provide a definition and categorization of OCC. Q3 addresses the role of building operators for OCC, while Q4 describes the implications for designers. Then, Q5 and Q6 discuss the role and possibilities of OCC for load flexibility, and for pandemic induced paradigm shifts in the built environment, respectively. In Q7, we provide a taxonomy and selection process of OCC, while Q8 details real world implementation case studies. Finally, Q9 explains the limits of OCC, and Q10 provides a vision for future research opportunities. Our findings offer valuable insights for researchers, practitioners, and policy makers, contributing to the ongoing discourse on the future of building operations management.

Green Building Assessment and Recommendations for Optimization Case Study of the Rectorate Building the University of Lampung

The Green Building Concept is a concept that offers a method of managing buildings with less energy and resources utilization to minimize the environmental impact. Since its implementation to be mandator, it is time for the University of Lampung to start implementing this concept. This research was aimed at assessing the Rectorate Building rating based on the national standard on Green Building Certification (Greenship-GBCI). There were several steps in conducting this research. Firstly, an opinion survey was conducted to measure the level of satisfaction of the building’s users. Secondly, a series of measurements related to lighting, temperature, humidity, noise, and air quality were carried out. Furthermore, an interview survey related to the operation and maintenance of the building were conducted. Finally, all of these data were entered in the Green Building rating tool for existing buildings. The results showed that the Rectorate Building score was 23 out of 117. This score is way below the score for a bronze rating of 35. Therefore, corrective action must be taken in order the building operation management to meet the Greenship-GBCI standard. Assessment also needs to be done on buildings througout the university for a reference in developing the University of Lampung Eco-campus program.

SolVis: A pilot study to facilitate understanding of real-time solar energy production through energy visualizations in learning environments

The transition to a carbon neutral society requires actions on all levels of society. Hereby, renewable energy - especially considering the current European energy crisis – such as solar energy will play an increasing role. To promote a more extensive use of solar energy and thus contributing to a faster energy transition, it is valuable to engage all citizens, not least children. How can real-time visualization of solar energy production in learning environments contribute to increased understanding of energy production and consumption? Educational environments can be a powerful platform to cultivate an interest in more environmentally and sustainable behaviour at an early age. However, energy is intangible, abstract, and difficult to grasp for non-experts. Digital tools incorporating energy visualization may be able to facilitate an increased understanding of energy units and measurements. The City of Gothenburg, Sweden, has the ambition to place photovoltaics on all roofs of schools and kindergartens, a process that is already in progress. The solar energy production is measured in real-time and only used for building operation management. This paper presents a study that develops a concept for a digital and educational visualization tool for school children aged 9 to 12, with the goal to engage, facilitate a lasting learning outcome, and awaken curiosity in solar energy. A prototype of a real-time visualization tool was developed, which in a playful way visualises the solar electricity production of the school property and compares it to the energy consumption of everyday activities, displayed on a centrally placed screen in the school. It was installed in four pilot schools and evaluated with 233 pupils. Results indicate several potentials of the visualization: to communicate local solar energy production to pupils, triggering thoughts, ideas, and a will to continue to learn more about solar energy production.

A SCIENCE MAPPING APPROACH BASED REVIEW OF MODEL PREDICTIVE CONTROL FOR SMART BUILDING OPERATION MANAGEMENT

Model predictive control (MPC) for smart building operation management has become an increasingly popular and important topic in the academic community. Based on a total of 202 journal articles extracted from Web of Science, this study adopted a science mapping approach to conduct a holistic review of the literature sample. Chronological trends, contributive journal sources, active scholars, influential documents, and frequent keywords of the literature sample were identified and analyzed using science mapping. Qualitative discussions were also conducted explore in details the objectives and data requirements of MPC implementation, different modeling approaches, common optimization methods, and associated model constraints. Three research gaps and future directions of MPC were presented: the selection and establishment of MPC central model, the capability and security of processing massive data, and the involvement of human factors. This study provides a big picture of existing research on MPC for smart building operations and presents findings that can serve as comprehensive guides for researchers and practitioners to connect current research with future trends.

Stakeholder-associated impact factors of building energy performance gap and their intersections: A social network analysis

The building energy performance gap is a critical obstacle to emissions reduction and energy conservation. Previous research mainly focused on identifying the factors contributing to the building energy performance gap (BEPG). Such studies have tended toward an inward-looking perspective that treats impact factors in isolation from one another. In reality, most factors are interrelated and associated with energy-related stakeholders. To address this research gap, this research developed a social network analysis model to investigate the underlying network of stakeholder-related influencing factors. According to nodal degree, status centrality, betweenness centrality, and brokerage roles, 21 factors and 14 links that substantially influence other factors directly or indirectly are identified. Six challenges, including “Challenges to knowledge and experience,” “Challenges in determining who is responsible for BEPG among stakeholders,” “Cost challenges,” “Poor quality challenges,” “Challenges to building operation management,” and “Information integrity challenges,” are further identified. Moreover, six strategies that are useful in controlling and eliminating influencing factors and interactions are proposed. The results show that, after the proposed strategies were conducted, the density and cohesion of the network are reduced by 48.28% and 11.62%, respectively, and the top ten betweenness centrality values for the nodes and links are reduced by at least 30%. This research is expected to aid not only researchers in better understanding the intricate interrelationships among the factors influencing BEPG but also practitioners in effectively controlling the influencing factors of BEPG.

Application of an occupant voting system for continuous occupant feedback on thermal and indoor air quality – Case studies in office spaces

Smartphone applications or electronic devices in the form of occupant voting systems (OVS) have demonstrated to be feasible for acquiring feedback from occupants to support the building operation manager to identify indoor environmental quality (IEQ) problems and inappropriate control settings of heating, ventilation and air-conditioning (HVAC). The present paper aims to contribute to the growing research on OVS as a tool to support building operation management. The paper presents a study about a tangible OVS, denoted TiAQ, that occupants could use to vote on their here-and-now experiences regarding the thermal environment and indoor air quality. The main objectives were to identify whether there was an alignment between the votes collected with TiAQ and the monitored IEQ variables as well as to demonstrate the use of occupant votes to identify problematic IEQ conditions and appropriate strategies for HVAC control. The study demonstrated that thermal votes collected with TiAQ could be related to the variations in the indoor temperature and ventilation airflow. Additionally, collected votes could identify i.a. poorly set indoor temperature setpoint and appropriate control strategies that could reduce energy use by 46% and increase thermal comfort and cold complaints by 6% if current airflow and indoor temperature setpoint were lowered.

A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

Short-term building energy predictions serve as one of the fundamental tasks in building operation management. While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions, few studies have addressed the potential data shortage problem in developing data-driven models. One promising solution is data augmentation, which aims to enrich existing building data resources for reliable predictive modeling. This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively. Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings. The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples, which in turns helps to enhance the accuracy in short-term building energy predictions. The average performance enhancement ratios in terms of CV-RMSE range between 12% and 18%. Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data. The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data

The fault detection and diagnosis (FDD) of air handling units (AHUs) serves as a major task in building operation management and energy savings. Data-driven classification methods have gained increasing popularities considering their flexibilities and effectiveness in practice. One essential challenge in developing accurate and reliable FDD classification models is the lack of sufficient labeled data. In practice, it can be highly time-consuming, labor-intensive and sometimes even infeasible to collected sufficient labeled data for all possible faulty operations. As a result, the fault detection models developed by limited and partially labeled data may not perform well in detecting any unknown or unseen faults in AHU operations. This study investigates the value of semi-supervised learning in detecting unseen faults during AHU operations. The main idea is to adopt a self-training strategy to gradually enhance the model capability by leveraging large amounts of unlabeled data. Data experiments have been designed to evaluate the unseen fault detection performance, the impacts of key semi-supervised learning parameters and the difficulties in detecting typical AHU faults. The insights obtained are valuable for the integration of data sciences with massive building operational data for smart building management.

Analysis of the status quo of green building operation management and countermeasures

Green buildings are of vital importance to reducing building energy consumption. In recent years, with the support and encouragement of many parties, green buildings have developed rapidly, and the number and scale of green building projects have been continuously expanded, which will undoubtedly become the future development t rend of the construction industry. However, the current level of operation and management of green buildings is low. Poor operation management and maintenance in the later stage have led to low utilization efficiency of various equipment used in green technologies. After a large number of green buildings are put into operation, they have not achieved the expected operating results. This article focuses on green building operation and management The status quo and problems of green building design innovation and the life cycle perspective of green building operation management related development strategies, in order to make up for the "shortcomings" of the expected value of green building public policy, improve the green level of green buildings, to achieve green Operational management makes theoretical contributions.

Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China

As one of the three major fields of building energy consumption, public buildings (PBs)are under pressure regarding energy saving and emission reductions, with PB energy consumption accounting for 38% of the total consumption. Thus, CO2 emissions released in PBs have become crucial for China in achieving its emission mitigation goal in the “Post Paris” period. This paper is the first to develop a support vector machine (SVM) method to predict and diagnose PB energy consumption based on 11 input parameters, including historical energy consumption data, climatic factors and time-cycle factors. Months with air-conditioning energy consumption in Wuhan were considered the study period, and we used June and July data for model prediction training, August data as the test set, and September data to diagnose the air conditioner energy consumption anomaly. The results show that air conditioning energy consumption was abnormal for four days in September. Relevant policies and suggestions are proposed based on the causal analysis. This research is expected to provide theoretical guidance and a practical data reference for building operations management.

Variable set points of glare control strategy for side-lit spaces: Daylight glare tolerance by time of day

Too much natural light in the form of glare causes visual discomfort of building occupants, and many owners choose direct shading for glare control in the building operation management. Traditionally, automated glare control strategy is based on a constant set point. Early findings approved that subjects’ glare sensations are not at a constant level, hence, simple shading control logic could be unreasonable and inaccurate. The purpose of this paper is to quantify the tendency of glare intolerance by the time of day, and based on which, the variable set points of glare control are proposed. 42 subjects participated in the subjective evaluation in a controlled laboratory setting from morning to afternoon on sunny days. The light environment parameters and HDR images in the subject’s view field were collected at each glare sensation vote. After statistical analysis, the experiment results shows that: (i) the results reinforce the previous findings that tolerance to discomfort glare increases as the day progresses; (ii) DGP metric defined thresholds for GSV (Glare Sensation Vote) scales were validated by this investigation in midday; (iii) the set points of Ev based glare control strategy should be 1700 lx for Noticeable/2400 lx for Disturbing/3500 lx for Intolerable in morning; 2000 lx for Noticeable/3000 lx for Disturbing/4000 lx for Intolerable in midday; 2200 lx for Noticeable/3200 lx for Disturbing/4200 lx for Intolerable in afternoon.

Utilizing BIM and Carbon Estimating Methods for Meaningful Data Representation

The building sector releases 36% of global CO2 emissions, with 66% of emissions occurring during the operation stage of the life cycle of a building. While current research focuses on using Building Information Modelling (BIM) for energy management of a building, there is little research on the visualizing building carbon emission data in BIM to support decision makings during operation phase. This paper proposes an approach for gathering, analyzing and visualizing building carbon emissions data by integrating BIM and carbon estimation models, to assist the building operation management teams in discovering carbon emissions problems and reducing total carbon emission. Data requirements, carbon emission estimation algorithms, integration mechanism with BIM are investigated in this paper. A case is used to demonstrate the proposed approach. The approach described in this paper provides the inhabitants important graphical representation of data to determine a buildings sustainability performance, and can allow policy makers and building managers to make informed decisions and respond quickly to emergency situations.

Ontologies to Support RFID‐Based Link between Virtual Models and Construction Components

Abstract: Virtual models have in recent years proven their worth in practice in building design. Today, virtual models of a complete building project are often created before the project is carried out in practice. The immediate advantages of this new working process are great; it reduces the number of errors, it gives a better production basis, and it improves clarity and enhances communication compared to traditional 2D drafting methods. However, there is still much unutilized potential in the virtual models, especially for use in the construction and operation phases. A digital link between the virtual models and the physical components in the construction process can improve the information handling and sharing in construction and building operation management. Such a link can be created by means of Radio Frequency Identification (RFID) technology. Ontologies play an important role as the foundation for information sharing between trading partners, reuse of data from one phase in construction to the next, integration of process and product models with enterprise resource planning (ERP) systems, easy access of information, communication of data through networks, reading of data stored in electronic tags, etc. This article reviews existing ontologies relevant in relation to creating such a digital link between virtual models and the physical components. The ontologies are reviewed from an ontology consumer (system developer) point of view. The ontologies are categorized according to their applicability to specification of technical services, resources, organizational relations, business processes, and overall frameworks for ontology descriptions and their relations. It is concluded, with a few modifications for industrial use, the technical service and resource ontologies are applicable and that the meta‐, organizational, and business process ontologies need further development and industrial maturity to be applicable for use in system development.

Integrating Operations Management Into Cost Systems: An Accounting Approach To Linear Programming

Managers rely on information from cost accounting systems in building operations management models to support their decision-making. Yet, the link between accounting and operations management techniques within a business information system can be improved. Cost accountants provide critical input for decision models, but, they often have difficulty using decision models and relating results back into the cost accounting information system. The Keano Macadamia Nut Company is a cross-functional exercise which illustrates a cost accounting system providing data for a linear programming problem; the use of linear programming to obtain a solution to a profit maximization problem, and a connection of the model’s optimal solution back into the cost accounting system.

Building operations management theory through case and field research

AbstractCase and field research studies continue to be rarely published in operations management journals, in spite of increased interest in reporting such types of studies and results. This paper documents the advantages and rigor of case/field research and argues that these methods are preferred to the more traditional rationalist methods of optimization, simulation, and statistical modeling for building new operations management theories. In the process of describing the constructs of inference and generalizability with reference to case research, we find the existing definitions inadequate and thus extend and refine them to better discriminate between alternate research methodologies. We also elaborate on methods for increasing the generalizability of both rationalist and case/field research studies. A major conclusion is that these alternate research methods are not mutually exclusive and, if combined, can offer greater potential for enhancing new theories than either method alone.