Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration
Abstract
:1. Introduction
2. Building Energy Management Systems (BEMSs)
- Increased energy efficiency;
- Improved environmental conditions;
- More efficient use of personnel;
- Enhanced fire, safety, and emergency procedures;
- Improved building performance standards;
- Streamlined building management;
- Reduced carbon emissions;
- Reduced overall costs through improved energy efficiency and personnel optimization.
- Higher initial design and installation costs;
- Potentially higher operation and maintenance costs compared to simpler systems;
- Requires experienced operators;
- Demands commitment at all levels throughout its operational life to sustain maximum efficiency.
- Data Collection and Building Survey:Gather all relevant building data, including architectural drawings, construction plans, specifications, and equipment details, and conduct a comprehensive on-site survey to collect information on building geometry, envelope properties, HVAC systems, lighting systems, occupancy patterns, and any other relevant parameters [34].
- Building Geometry Modeling:Use the collected data to create a 3D digital model of the building’s geometry. This model represents the physical layout and structure of the building, including floors, walls, windows, doors, and roofs [35].
- Building Energy System Modeling:Develop detailed models of the building’s energy systems, including HVAC systems, lighting systems, and other energy-consuming equipment, and specify the characteristics and properties of each component, such as the efficiency of chillers, boilers, air handling units, lighting fixtures, etc. [36].
- Energy Simulation Software:Choose an appropriate energy simulation software that can integrate the building geometry and energy system models. Popular simulation tools include EnergyPlus, eQUEST, and DOE-2, which can analyze the building’s energy consumption under different conditions and schedules [37].
- Weather Data and Occupancy Profiles:Import local weather data to simulate the building’s energy performance under different climate conditions and define occupancy profiles to account for variations in internal heat gains based on the building’s usage and occupancy patterns [38].
- Simulation and Calibration:Run the energy simulation using the chosen software to analyze the building’s energy performance. Compare the simulation results with actual utility bills or historical energy consumption data to calibrate the model and ensure its accuracy.
- BEMS Integration:Integrate the calibrated energy model with the BEMS. The BEMS may include sensors, meters, and control algorithms to monitor and manage the building’s energy consumption in real-time.
- Sensitivity Analysis and Optimization:Perform sensitivity analyses to evaluate the impact of different building parameters and design options on energy consumption. Optimize the BEMS settings and control strategies to achieve energy efficiency and occupant comfort goals.
- Validation and Commissioning:Validate the BEMS model by comparing its performance against real-world data after the system is installed and operational. Commission the BEMS to ensure it functions as intended and meets the desired energy efficiency and control objectives.
- Continuous Monitoring and Maintenance:After implementation, continuously monitor the BEMS and the building’s energy performance to identify any discrepancies or potential improvements. Regularly update the model with new data and changes to the building’s systems to maintain its accuracy over time.
- Building Typology:The research primarily concentrates on BEMS studies related to residential buildings, with commercial, educational, and office buildings also receiving some attention. However, industrial and institutional buildings have been relatively neglected in the literature. The prevalence of energy inefficiencies in domestic households is highlighted as a significant concern.
- Building Services Subsystems:The major focus in BEMS studies has been on HVAC systems, underscoring the significant contribution of heating and cooling demands to energy consumption in South Africa. Lighting systems and consumer electronics are other areas of interest, but they have not received as much attention as HVAC systems.
- Applied BEMS Strategies:DSM emerged as the most widely adopted strategy, especially in residential buildings, to address energy management challenges. In commercial buildings, MPC was utilized to tackle real-time electricity pricing issues. However, optimization methods and FDD were relatively underrepresented, despite their potential for enhancing energy efficiency.
- Methodological Approaches and Testbeds:Classical techniques such as linear programming and linear regression have been commonly used in BEMS studies. To overcome the limitations of these traditional approaches, researchers have applied metaheuristic methods like genetic algorithms (GAs). Additionally, AI and ML techniques, such as deep learning neural networks, have been employed for energy demand prediction and optimization. The use of simulation testbeds for validation is predominant. Gaps in this section include limited integration of big data analytics, a lack of hybrid methodologies, insufficient consideration of uncertainty and sensitivity analysis, and the need for real-time performance assessment in actual operational conditions.
- Focused Energy Management System Tasks:BEMS studies primarily concentrate on control functions, monitoring and evaluation, and analyzing and predicting current and future energy use. Optimizing energy efficiency within the operational stage of buildings has emerged as a key priority. However, the gaps involve the underrepresentation of demand response strategies, limited integration of renewable energy sources (RESs), neglect of occupant behavior modeling, and a lack of multi-objective optimization approaches to balance conflicting objectives.
3. Building Information Modeling (BIM)
- Facility Management and Maintenance:BIM provides a comprehensive database of information about the building, including architectural, structural, and mechanical, electrical, and plumbing (MEP) details. These data can be used by facility managers and maintenance teams to effectively manage the building’s assets, plan preventive maintenance, and quickly access information about various systems and components [41].
- Space Management:BIM allows for accurate space management, enabling facility managers to visualize and track spaces within the building, assign uses, and manage occupancy. This helps to optimize space utilization, track changes in real-time, and plan for future space requirements [42].
- Energy Efficiency and Sustainability:
- Asset Tracking and Inventory Management:BIM can be integrated with asset tracking systems, allowing facility managers to monitor and maintain equipment and inventory efficiently. This integration streamlines the process of tracking assets, automating inventory management, and optimizing procurement processes.
- Emergency Planning and Safety Management:BIM can assist in emergency planning by providing visual and data-rich representations of the building’s layout, escape routes, and safety equipment locations. This information aids in developing effective emergency response plans and conducting simulations to assess safety measures [14].
- Upgrades and Retrofits:During the operational phase, buildings may require upgrades or retrofits. BIM’s detailed information and visualization capabilities help architects and engineers plan and execute these projects with greater accuracy and minimal disruption to the building’s occupants.
- Collaboration and Communication:BIM facilitates collaboration among various stakeholders, including architects, engineers, contractors, facility managers, and owners. It serves as a centralized platform for sharing information, making revisions, and documenting changes, ensuring everyone involved has access to the most up-to-date data.
- Data for Decision Making:BIM generates valuable data and insights throughout a building’s lifecycle. By analyzing these data, building operators can identify trends, predict maintenance needs, and make informed decisions to optimize performance and reduce operational costs [6].
4. Methodology
- Initiation involved conducting a search based on the field of interest, encompassing title, abstract, keywords, and publication year.
- Subsequently, the outcomes of the initial search were meticulously refined to ensure pertinence and suitability.
- The final selection of studies transpired through a dual process of visual screening and citation analysis, meticulously guaranteeing the inclusion of pertinent and influential works within the review.
5. Results and Discussion
- Energy consumption reduction by empowering stakeholders to make well-informed decisions from the outset. Architects and engineers can utilize real-time BEMS data during the design phase to tailor building orientation, system sizing, and layout to maximize energy efficiency. This data-driven design approach ensures alignment with actual operational parameters, creating a solid foundation for buildings that inherently prioritize energy conservation.
- Real-time performance monitoring and management, which contributes to prompt energy-saving interventions. By integrating BEMS data into BIM models, facility managers gain continuous insights into energy consumption patterns and system efficiencies. This real-time feedback loop enables the early identification of inefficiencies or deviations, facilitating proactive measures to prevent energy wastage and ensuring optimal system performance.
- An enhancement in the accuracy of energy performance predictions through dynamic simulations, a hallmark of the BEMS-BIM integration. By incorporating real-time operational data and occupant behavior, these simulations provide a realistic representation of how building systems will function. The result is a more reliable estimation of energy usage during the design phase, ensuring that energy-efficient strategies are grounded in empirical data and better poised to translate into operational efficiency.
- Cost savings as a significant financial advantage of BEMS-BIM integration. Reduced energy consumption translates directly into lower operational costs over the building’s lifespan. The integration assists in optimal system sizing, mitigating the oversizing that often leads to capital and operational inefficiencies. Timely fault detection and maintenance practices, guided by real-time data, minimize downtime, yielding not only operational cost savings but also improved occupant satisfaction.
- Retrofitting and upgrades by using the integration’s insights to guide well-informed decisions on energy-saving measures, ensuring that resources are allocated efficiently to generate maximum impact. Furthermore, streamlined collaboration among stakeholders, fostered by BEMS-BIM integration, minimizes errors, reworks, and associated costs throughout the project’s lifecycle.
- Clear Project Goals and Requirements:Define clear project goals and requirements for the integration of BEMS and BIM. Understand the specific objectives, data exchange needs, and desired outcomes. This will provide a strong foundation for the integration process and help guide decisions throughout the project.
- Standardized Data Formats and Protocols:Ensure that both BEMS and BIM systems use standardized data formats and protocols for data exchange. Common standards such as Industry Foundation Classes (IFCs), Building Controls Virtual Test Bed, and Building Automation and Control Networks can facilitate seamless communication between systems. Implementing these standards reduces compatibility issues.
- Open Application Programming Interfaces (APIs):Utilize open APIs to enable data sharing and communication between BEMS and BIM systems. Open APIs allow developers to create custom integrations that suit specific project needs. This approach promotes flexibility and scalability, making it easier to adapt to changes in technology and system requirements.
- Interdisciplinary Collaboration:Foster collaboration between various project stakeholders, including architects, engineers, facility managers, and IT professionals. Interdisciplinary teamwork ensures that different perspectives are considered, leading to a more holistic integration approach. Regular communication and coordination can address technical challenges and enhance the overall integration process.
- Data Mapping and Transformation:Develop a clear data mapping and transformation strategy to align data structures between BEMS and BIM systems. This involves identifying equivalent data fields, units, and semantics to enable accurate data exchange. Data transformation tools or middleware can be employed to facilitate this process and ensure consistency across systems.
- Pilot Testing and Validation:Before full-scale implementation, conduct pilot testing to validate the integration approach. Use a smaller-scale project or a subset of data to identify and address any technical issues or bottlenecks. Pilot testing helps refine the integration process and minimizes risks associated with large-scale deployment.
- The creation of a BIM model:A BIM model is created by the BIM designer based on the building’s structure using a BIM authoring system. This model includes information and geometric object data, as well as spatial relationships between objects. To ensure compatibility and ease of use, the model is exported using the IFC format, which is supported by various BIM software manufacturers.
- The creation of the BEMS model:Simultaneously with the creation of the BIM model, the BEMS designer processes information related to the BEMS. A comprehensive list of all data points is generated, with automatic generation possible for most systems. However, manual enrichment of data with additional information may be required. Each data point is then mapped to a specific device in the BIM model.
- The optimization and updating of the BEMS model:Once the BEMS model is created, it is essential to ensure the correct functioning of the energy data used. Regular updates and adjustments become necessary during renovations, layout changes, or sensor replacements. Such updates are typically localized and do not significantly affect the overall model structure. The model maintenance workflow involves fewer but similar tasks compared to the model creation workflow. Multiple actors, including BIM and BEMS modelers, may be involved in these updates. Before integrating the BEMS, data quality from individual measurement points must be verified and then updated in the BEMS model.
- Performance-Driven Design:Challenges in performance-driven design encompass generation, simulation, and optimization. Architects must develop algorithms for design parameter adjustment, enhancing generative design. Performance-driven methods emphasize conceptual logic over outcomes, yet current algorithms lack geometric diversity. Integrating inner space topology with energy/ventilation performance remains underexplored.
- Model-Based Operational Performance Optimization:Operational optimization relies on efficient simulations for energy savings. Ensuring computation speed aligns with real-time control constraints is essential. Balancing computation speed and model accuracy is a key challenge, warranting investigation.
- Integrated Simulation for Digital Twins:Digital twins demand data integrity and real-time simulations. Data quality, cost-effective sensor use, and real-time simulations are priorities. Scaling simulations for urban use requires diverse data integration.
- Building Simulation for Urban Energy Planning:Urban energy modeling’s accuracy can improve through uncertainty analysis and model calibration. Integrating UBEM with urban energy systems enhances energy infrastructure modeling.
- Building-to-Grid Interaction Modeling:Enhancing building–grid interaction modeling entails leveraging flexibility and adapting to rapid grid changes. Addressing occupancy impact on energy consumption and exploring larger-scale simulations and control are critical.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AEMS | Advanced Energy Management System |
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
API | Application Programming Interface |
BEMS | Building Energy Management System |
BES | Building Energy Simulation |
BIM | Building Information Modeling |
CSV | Comma-Separated Values |
DNN | Deep Neural Network |
DSM | Demand-Side Management |
DT | Digital Twin |
FDD | Fault Detection and Diagnosis |
GA | Genetic Algorithm |
HVAC | Heating, Ventilation, and Air Conditioning |
IFC | Industry Foundation Class |
IoT | Internet of Things |
LSTM | Long Short-Term Memory |
MEP | Mechanical, Electrical, Plumbing |
ML | Machine Learning |
MPC | Model Predictive Control |
RES | Renewable Energy Source |
VPP | Virtual Power Plant |
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Journal Name | Number of Publications |
---|---|
Energy and Buildings | 30 |
Energies | 27 |
Energy Procedia | 12 |
Journal of the Architectural | 12 |
Institute of Korea Planing Design | |
Sustainability | 11 |
Applied Mechanics AND Materials | 9 |
IEEE Industrial Electronics Society | 9 |
IFAC-PapersOnLine | 7 |
6TH International Building | 6 |
Physics Conference IBPC 2015 | |
Applied Energy | 6 |
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Kozlovska, M.; Petkanic, S.; Vranay, F.; Vranay, D. Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration. Energies 2023, 16, 6327. https://doi.org/10.3390/en16176327
Kozlovska M, Petkanic S, Vranay F, Vranay D. Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration. Energies. 2023; 16(17):6327. https://doi.org/10.3390/en16176327
Chicago/Turabian StyleKozlovska, Maria, Stefan Petkanic, Frantisek Vranay, and Dominik Vranay. 2023. "Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration" Energies 16, no. 17: 6327. https://doi.org/10.3390/en16176327