Simple Model for the Theoretical Survey of the Green Roof Thermal Behavior

A green roof is an option for the improvement of a building thermal comfort. The objective of this work is to compare respective behaviors of a green roof and a concrete watertight roof vis-à-vis external requests in wet tropical zones. The canopy being considered as uniform, the electric analogy method was used to establish the mathematical models associated to both studied systems of roof. Based on these models, a Matlab computing code was worked out. It ensues from simulation results that in diurnal period, the green roof concrete support top face temperature is lower than that of the concrete watertight roof top face, whereas in night-period the opposite occurs. These results which highlight the energy benefit of the green roof are in agreement with the experimental measurement results obtained at the Laboratory PIMENT of the University of La Reunion. Besides, results of sensitivity analysis done with Fourier Amplitude Sensitivity Test enabled to identify a certain number of the most influential parameters of the proposed model. The above mentioned computing code also forms a help tool for the choice of plants to be experimented on the green roof

A modular, open system for testing ventilation and cooling strategies in extremely low energy lecture rooms

Lecture rooms with their high, quickly fluctuating internal gains, e.g. changing from no occupation to full occupation within some minutes, are quite challenging when good indoor air-quality and thermal comfort is required in an extremely low energy building context. One essential aspect is the perfect control of air flow and temperature based on reliable, continuous measurement in all relevant parts of the ventilation system. This paper describes a case study that combines real building operation on a university campus with an advanced, modular test platform covering all aspects of the real operational performance of extremely low energy lecture rooms. A full-scale Passive House test facility was constructed at Technology Campus Gent, KU Leuven, Belgium. The building is part of the campus and has two lecture rooms for 80 students each. Designed and certified according to the Passive House standard, the new facility consists of two levels, constructed on top of an existing building (ground-floor only). Thermal insulation was placed also in-between the two lecture rooms and in the internal walls towards the staircase. This results in a layout with two identical, box-shaped volumes with different thermal mass (one with a timber-frame structure, one with brick-walls). The facility includes a high performance AHU, with frequency controlled fans and two sets of VAV-boxes, providing ventilation, heating and cooling, a wood pellet boiler, motor-controlled exterior sun-shading and windows and high performance lighting fixtures with daylight control. A key feature is the integrated system for monitoring and control based on open standards: BACnet for communication with the AHU, KNX, DALI and Ethercat to link decentralized IO-units with the Building controler. This PLC based, distributed PC environment provides detailed control of the building equipment and real-time, long-term monitoring of all building parameters and the outdoor climate. It provides also a very flexible and powerful platform for the implementation and testing of new strategies for model based predictive control (MPC) and fault detection and diagnosis (FDD). The Modelica language is used for building simulation during operation. A detailed Building Information Model (BIM) was created and all relevant elements of the equipment and the BMS will be added. The BIM will be used to manage measured data and provide integration between simulation and measurement. Results from detailed air flow measurements at different fan speeds are provided. These initial measurements show good general agreement and provide deeper insight in the dynamic behaviour of the ventilation system. Beside the air flow sensors of the AHU and the VAV boxes, Venturi tubes are integrated in the supply- and return-air duct of each lecture room. The modular monitoring system provides the possibility of easy integration of additional sensors (e.g. thermo-anemometers for temporary measurement of velocities and calculation of the air flow based on the Log-Tchebychev method). Different cooling strategies will be tested and compared as soon as climate conditions permit.status: publishe

An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

© 2018 Elsevier B.V. The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers’ poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts’ involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components’ control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems.status: publishe

Sensor handling in Building Information Models. Development of a method and application on a case study

Considering the rapidly growing application of Building Information Modelling (BIM) for all Lifecycle stages of buildings and the use of BIM as a natural information source for Building Energy Simulation (BES) models, this paper presents a complementary aspect: The integration of handling of measured performance data into a BIM-based workflow. The proposed method facilitates the comparison of the actual, measured performance of buildings with the predicted performance, based on simulation results. It complements existing BIM to BES interface methods that help to transfer geometry and material information, by providing easier access to measured input-data (e.g. weather data and building occupancy) and measured performance. The method is applied to a full-scale test facility located on the Technology Campus Gent of KU Leuven (Belgium). The main aim here is to facilitate the calibration of BES models and the development, implementation and test of model- And rule-based fault detection methods (FDD) and model predictive control strategies (MPC).status: publishe

Automated grey box model implementation using BIM and Modelica

© 2019 Elsevier B.V. A large part of energy usage in buildings occurs during the operational phase, emphasising the need for efficient and improved facility management, operation and control. Model Predictive Control (MPC) or Fault Detection and Diagnosis (FDD) are among the strategies that allow minimising energy use and costs during operation. However, the need for fast and accurate dynamic models (e.g. grey box model), which are time-consuming and challenging to implement, precludes their systematic integration in the built environment. A typical grey-box modelling approach consists of manually implementing several grey-box model structures with an increasing level of complexity before performing a forward selection procedure to identify the optimal configuration. The link between the different grey-box models and the monitored data is also established manually. Such an approach can be both time-consuming and error-prone and involves a significant cost that hampers the broad adoption of strategies such as MPC and FDD. This study proposes a tool-chain that uses BIM to automatically generate several grey-box structures with added complexities stemming from the specific geometry and design of the building. More specifically, an existing rule-based IFC to Modelica interface is extended to automatically create several Modelica-based grey box models that gradually take into account the building's specific information and characteristics. Additional rules are also proposed to automate the connection between the models and the building monitoring system. As a forward selection approach, a multi-objective optimisation using the NSGA-2 algorithm is adopted. The application of the tool-chain on two case studies shows that the integration of BIM to automate the implementation of grey box models, not only reduces the human involvement in the modelling process but can also produce more accurate models. Besides, this study shows that the use of multi-objective optimisation with datasets from two different seasons results in models that are valid for all seasons.status: accepte

AN OPEN IFC TO MODELICA WORKFLOW FOR ENERGY PERFORMANCE ANALYSIS USING THE INTEGRATED DISTRICT ENERGY ASSESSMENT BY SIMULATION (IDEAS) LIBRARY

Building Energy Performance Simulations (BEPS) become more and more common in the design process of energy efficient buildings. Despite the many attempts to improve the user friendliness of these applications there is still an important effort required to create a well-adapted model. One of the main issues is the necessity to re-introduce manually data originating from different sources: project specific geometry as well as component and material properties from manufacturers. This tedious and error prone process can lead to poor judgment or input errors that can have significant impact on the correctness of the model predictions. In order to tackle these issues, studies have recently set their focus on the use of Building Information Modelling (BIM) and its standard format Industry Foundation Classes (IFC) to (partially) automate the BEPS model creation. Nonetheless, most of the existing tools relying on this concept are either commercial software, complex to use for a non BIM expert or still under development. To facilitate the use of an IFC model as a base for BEPS model creation, the focus of this research is to implement and test an open and easy to use PYTHON package which maps IFC objects and their relations to objects of a Modelica library to assist the BEPS modeller. The PYTHON package is intended to reduce the workload and human involvement within the modelling process by generating automatically a “pre-model”. This “pre-model” is a semi- configured BEPS obtained from the IFC to Modelica mapping and contains the relevant information retrieved from the BIM including building geometry, spaces together with a description of their function and occupation, material properties, HVAC components with their properties and how they are connected,… Nevertheless, not all required information for the BEPS-model implementation is present in the BIM and the modeller might want to reduce model complexity, especially in early design stages, or introduce additional aspects. The necessity to manually introduce missing data and to adapt and correct the “pre-model” to obtain the final BEPS-model motivated the use of Modelica which facilitates model modification from top to component level. A two-phase approach was implemented: first the IFC model is parsed, relevant information is extracted and a “space topology” based on the space boundaries characteristics defined in the BIM is generated. In the second step the “pre-model” for building and systems is created using a template corresponding to the Modelica library structure. To investigate the effectiveness and robustness of the implementation, a step-by-step validation was performed. Starting from a simple four walls building a rigorous checking of the mapping consistency was undertaken. Gradually the complexity level of the test cases was increased. A successful mapping to the IDEAS Modelica library was achieved for the BuildingSMART duplex apartment building case. Finally the method was applied to a university building with detailed monitoring data available. The obtained BEPS-model was calibrated and used for fault detection and diagnosis. Despite some limitations of the current implementation, the PYTHON package significantly improves the process of BEPS-model creation for the studied building and its HVAC system.status: publishe

An automated IFC-based workflow for building energy performance simulation with Modelica

Steadily increasing use of Building Information Modelling (BIM) in all phases of building's lifecycle, together with more attention for openBIM and growing software support for the most recent version of the Industry Foundation Classes (IFC 4) have created a very promising context for an even broader application of Building Energy Performance Simulation (BEPS). At the same time, an urgent need for modelling guidelines and standardisation becomes evident. A well-defined BIM-based workflow and a set of tools that fully exploit and extend the possibilities of the openBIM-technology can make the difference when it comes to reliability and cost of BEPS to design, build and operate high-performance buildings. This paper describes the essential elements of this integrated workflow, explains why openBIM comprises much more than just a standardized file-format and what is achieved with the already available technology, namely the Information Delivery Manual (IDM) and a newly developed Model View Definition. This MVD is tailored to the needs of Building Energy Performance Simulation (BEPS) that uses the Modelica language together with a specific library (IDEAS) and can easily be adapted to other libraries. In this project, several tools have been developed to closely integrate BEPS and IFC4. The simulation engine now gets the vast majority of the required input directly from the IFC4-file. For the implementation of the tools, the PYTHON language and the open source library IfcOpenShell are used. A case study is presented, that was used for extensive tests of the proposed approach and the implemented tools. The essential benefits of this new workflow are illustrated, and the feasibility is demonstrated. Opportunities and remaining bottlenecks are identified to encourage further development of BIM software to fully support IFC4 as an information source for BEPS. Beside some improvements of the proprietary class structure and functionality, enabling the export of IFC4 files based on custom MVDs is one required key feature.status: publishe

A combined scientometric and conventional literature review to grasp the entire BIM knowledge and its integration with energy simulation

© 2019 Elsevier Ltd This paper presents an up to date overview of the principal research topics and research trends within the Building Information Model (BIM) research domain. It also offers a detailed review of the integration of BIM and Building Energy Performance Simulation (BEPS). The different strategies to improve interoperability are reviewed together with the various applications of such an integration (BIM with BEPS) in the literature. Firstly, a scientometric analysis which allows identifying research patterns and emerging trends in a specific research domain is performed to categorise the large number of articles constituting BIM literature into several clusters, each representing a particular topic. The main research topic in each cluster, together with the chronological progress and evolution of each cluster are summarized through a literature review of the selected highly cited articles. Secondly, an analysis of the different aspects relevant to the integration of BIM with BEPS is performed to highlight the evolution of the interoperability between BIM and energy simulation tools. Subsequently, a review of the different applications of such integration (BIM with BEPS) is performed to identify potential knowledge gaps. This study highlights six main BIM research topics focusing on BIM adoption and benefits, BIM-aided management, progress monitoring and as-built modelling, interoperability, life cycle analysis and energy simulation. It also emphasises the lack of well-established strategies to ensure the interoperability between BIM and energy simulation tools. Furthermore, this study reports on the poor integration of BIM and BEPS for building system and control modelling as well as its limited application during the operational phase.status: Published onlin

Towards an IFC-Modelica Tool Facilitating Model Complexity Selection for Building Energy Simulation

This paper presents a novel tool chain that enables a direct coupling between building information models (BIM) and building energy simulation (BES) models developed in Modelica. In contrast to similar tools found in literature, the novel tool chain is a Python implementation that provides a direct coupling between BIM and Modelica and focuses on the thermal modelling of building envelopes. The main innovation in this tool chain is that it provides increased user flexibility by facilitating a BES model generation with different levels of complexity, which is demonstrated on a real-life experiment. In this case study, significant savings in simulation time were obtained without significantly affecting the model accuracy in terms of predicting energy use and load-duration curves, if the aggregation of zones reflects the control strategy for space heating.status: publishe

Thermal Behavior of Green Roof in Reunion Island: Contribution Towards a Net Zero Building

International audienceA green roof is an option for improving a building thermal comfort. The investigation is here performed within the specific climate context of Reunion Island in south hemisphere. This type of roof system involves choice difficulties for plant species that are more favorable to establish that comfort. The objective of this work is to simulate the dynamic behavior of this system towards external requests in wet tropical zones and vis-à-vis influences of certain number of physical parameters related to this system. As long as possible, authors used the electrical analogy method to establish a mathematical model associated to the studied system. Based on this model, a Matlab computing code was finalized. Weather data of Reunion Island were used for simulations; the green roof potential and benefit were highlighted by surveying the temperature gain and the heat flux crossing the roof as well as the energy saving performance. Furthermore, the energy consumption being surveyed while doing sensitivity analysis with Fourier Amplitude Sensitivity Test method, the most influential parameters of the model were identified. Full scale experimental results are provided and consisting in monitoring green roof on the top of a public building. According to the results, we can assert that the green roof decreases heat flux entering through the roof during the day and restrains the restoration of accumulated heat at night. Indeed, the support on which the plantation ground bases affects the building thermal insulation. The experimental data are also conducted to prove the effectiveness of thermal insulation by green roofs in reducing temperature in the building between 5°C and 7°C in relation to plants type and the canopy Leaf Area Index (LAI). A comparison of experimental values and model results is done. Among other uses, this code can be used as a tool for choosing the plants and the drain materials to be experimented on the green roof. The results offer hints to optimize the design and thermal performance of extensive green roofs