Duration
Part 1 : 15h Th, 15h Pr
Part 2 : 15h Th, 25h Pr, 70h Proj.
Number of credits
Lecturer
Part 1 : Shady Attia
Part 2 : Shady Attia
Coordinator
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Given the increasing demand for more sustainable, high-performance and resilient buildings,together with the growing complexity of integrated design solutions, architectural engineersneed to be able to make informed design decisions based on an understanding of theinteractions between climate, building form and envelope, energy systems, controls, occupants,and the urban environment.
This course presents the theoretical and operational principles of building performancesimulation and monitoring. It introduces performance-based analysis as a tool for designsupport and data-driven decision-making.
The course covers data collection and analysis, energy model development, definition ofboundary conditions and simulation assumptions, thermal zoning, envelope and systemsmodeling, model verification, calibration using measured data, uncertainty analysis, and criticalinterpretation of simulation results.
Students apply these principles through practical exercises and a real-building case study. Theyconduct a building audit and monitoring campaign, develop and calibrate a simulation modelusing DesignBuilder, assess its energy and environmental performance, and develop a fullyelectric renovation scenario integrating energy-efficiency measures, renewable energy, storage,and measures to ensure good indoor environmental quality.
This reflects the revised course much more closely, including the audit, monitoring, calibratedbase model, and full-electric renovation scenario described in the new document.
Part 1
Given the increasing demand for more sustainable, high-performance and resilient buildings,together with the growing complexity of integrated design solutions, architectural engineersneed to be able to make informed design decisions based on an understanding of theinteractions between climate, building form and envelope, energy systems, controls, occupants,and the urban environment.
This course presents the theoretical and operational principles of building performancesimulation and monitoring. It introduces performance-based analysis as a tool for designsupport and data-driven decision-making.
The course covers data collection and analysis, energy model development, definition ofboundary conditions and simulation assumptions, thermal zoning, envelope and systemsmodeling, model verification, calibration using measured data, uncertainty analysis, and criticalinterpretation of simulation results.
Students apply these principles through practical exercises and a real-building case study. Theyconduct a building audit and monitoring campaign, develop and calibrate a simulation modelusing DesignBuilder, assess its energy and environmental performance, and develop a fullyelectric renovation scenario integrating energy-efficiency measures, renewable energy, storage,and measures to ensure good indoor environmental quality.
This reflects the revised course much more closely, including the audit, monitoring, calibratedbase model, and full-electric renovation scenario described in the new document.
Part 2
Given the increasing demand for more sustainable, high-performance and resilient buildings, together with the growing complexity of integrated design solutions, architectural engineers need to be able to make informed design decisions based on an understanding of the interactions between climate, building form and envelope, energy systems, controls, occupants, and the urban environment.
This course presents the theoretical and operational principles of building performance simulation and monitoring. It introduces performance-based analysis as a tool for design support and data-driven decision-making.
The course covers data collection and analysis, energy model development, definition of boundary conditions and simulation assumptions, thermal zoning, envelope and systems modeling, model verification, calibration using measured data, uncertainty analysis, and critical interpretation of simulation results.
Students apply these principles through practical exercises and a real-building case study. They conduct a building audit and monitoring campaign, develop and calibrate a simulation model using DesignBuilder, assess its energy and environmental performance, and develop a fully electric renovation scenario integrating energy-efficiency measures, renewable energy, storage, and measures to ensure good indoor environmental quality.
This reflects the revised course much more closely, including the audit, monitoring, calibrated base model, and full-electric renovation scenario described in the new document.
Learning outcomes of the learning unit
By the end of the course, students will be able to:
Explain the fundamental principles of building performance simulation and monitoring.
Collect, analyze, and interpret the data required to characterize the energy andenvironmental performance of a building.
Develop a coherent building energy simulation model in DesignBuilder by defininggeometry, thermal zoning, envelope characteristics, internal loads, ventilation, systems,and operational conditions.
Apply model verification and quality assurance procedures, including BESTEST, toidentify errors and inconsistencies in simulation models.
Calibrate a simulation model using measured data and assess its accuracy, level ofconfidence, and main sources of uncertainty.
Analyze and interpret simulation results related to energy use, greenhouse gasemissions, thermal comfort, and indoor environmental quality.
Compare different design measures and evaluate their influence on buildingperformance.
Develop and evaluate a fully electric renovation scenario integrating energy efficiency,renewable energy, electrical and thermal storage, heat-recovery ventilation, andoverheating protection.
Formulate performance-based design recommendations based on simulation results andmeasured data.
Part 1
By the end of the course, students will be able to:
Explain the fundamental principles of building performance simulation and monitoring.
Collect, analyze, and interpret the data required to characterize the energy andenvironmental performance of a building.
Develop a coherent building energy simulation model in DesignBuilder by defininggeometry, thermal zoning, envelope characteristics, internal loads, ventilation, systems,and operational conditions.
Apply model verification and quality assurance procedures, including BESTEST, toidentify errors and inconsistencies in simulation models.
Calibrate a simulation model using measured data and assess its accuracy, level ofconfidence, and main sources of uncertainty.
Analyze and interpret simulation results related to energy use, greenhouse gasemissions, thermal comfort, and indoor environmental quality.
Compare different design measures and evaluate their influence on buildingperformance.
Develop and evaluate a fully electric renovation scenario integrating energy efficiency,renewable energy, electrical and thermal storage, heat-recovery ventilation, andoverheating protection.
Formulate performance-based design recommendations based on simulation results andmeasured data.
Part 2
By the end of the course, students will be able to:
- Explain the fundamental principles of building performance simulation and monitoring.
- Collect, analyze, and interpret the data required to characterize the energy and environmental performance of a building.
- Develop a coherent building energy simulation model in DesignBuilder by defining geometry, thermal zoning, envelope characteristics, internal loads, ventilation, systems, and operational conditions.
- Apply model verification and quality assurance procedures, including BESTEST, to identify errors and inconsistencies in simulation models.
- Calibrate a simulation model using measured data and assess its accuracy, level of confidence, and main sources of uncertainty.
- Analyze and interpret simulation results related to energy use, greenhouse gas emissions, thermal comfort, and indoor environmental quality.
- Compare different design measures and evaluate their influence on building performance.
- Develop and evaluate a fully electric renovation scenario integrating energy efficiency, renewable energy, electrical and thermal storage, heat-recovery ventilation, and overheating protection.
- Formulate performance-based design recommendations based on simulation results and measured data.
Prerequisite knowledge and skills
Basic knowledge of building physics, particularly heat transfer, building thermal performance,and energy systems, is required.
The course is taught in English. Sufficient proficiency in scientific and technical English isrequired to follow the lectures, use the software and manuals, read scientific papers, andcomplete the required assignments.
This is cleaner than keeping a reference to a specific course code that may no longer beapplicable.
Part 1
Basic knowledge of building physics, particularly heat transfer, building thermal performance,and energy systems, is required.
The course is taught in English. Sufficient proficiency in scientific and technical English isrequired to follow the lectures, use the software and manuals, read scientific papers, andcomplete the required assignments.
This is cleaner than keeping a reference to a specific course code that may no longer beapplicable.
Part 2
Basic knowledge of building physics, particularly heat transfer, building thermal performance, and energy systems, is required.
The course is taught in English. Sufficient proficiency in scientific and technical English is required to follow the lectures, use the software and manuals, read scientific papers, and complete the required assignments.
This is cleaner than keeping a reference to a specific course code that may no longer be applicable.
Planned learning activities and teaching methods
The course combines theoretical lectures, discussions of scientific papers, self-study, practicalsimulation and monitoring exercises, a BESTEST-based group exercise, and a group projectbased on a real-building case study.
Students progressively apply the concepts introduced during the course to data collection andanalysis, simulation model development, model verification and calibration using measureddata, and the evaluation of renovation strategies.
The case study enables students to integrate the acquired knowledge through the audit,monitoring, modeling, and performance analysis of a real residential building.
This stays consistent with the updated case study, which specifically requires a real occupiedresidential unit, monitoring, simulation, and calibration.
Part 1
The course combines theoretical lectures, discussions of scientific papers, self-study, practicalsimulation and monitoring exercises, a BESTEST-based group exercise, and a group projectbased on a real-building case study.
Students progressively apply the concepts introduced during the course to data collection andanalysis, simulation model development, model verification and calibration using measureddata, and the evaluation of renovation strategies.
The case study enables students to integrate the acquired knowledge through the audit,monitoring, modeling, and performance analysis of a real residential building.
This stays consistent with the updated case study, which specifically requires a real occupiedresidential unit, monitoring, simulation, and calibration.
Part 2
The course combines theoretical lectures, discussions of scientific papers, self-study, practical simulation and monitoring exercises, a BESTEST-based group exercise, and a group project based on a real-building case study.
Students progressively apply the concepts introduced during the course to data collection and analysis, simulation model development, model verification and calibration using measured data, and the evaluation of renovation strategies.
The case study enables students to integrate the acquired knowledge through the audit, monitoring, modeling, and performance analysis of a real residential building.
This stays consistent with the updated case study, which specifically requires a real occupied residential unit, monitoring, simulation, and calibration.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Face-to-face course
Further information:
Face-to-face course.
Lectures, discussions, practical exercises, simulation sessions, and activities related tothe case study are conducted face-to-face.
Active student participation is expected. Attendance is compulsory for the BESTESTgroup exercise sessions and the case study briefing.
The compulsory attendance for the BESTEST group exercise and case-study briefing isexplicitly stated in the revised course document.
Part 1
Face-to-face course
Further information:
Face-to-face course
Further information:
Face-to-face course.
Lectures, discussions, practical exercises, simulation sessions, and activities related tothe case study are conducted face-to-face.
Active student participation is expected. Attendance is compulsory for the BESTESTgroup exercise sessions and the case study briefing.
The compulsory attendance for the BESTEST group
Part 2
Face-to-face course
Further information:
- Face-to-face course.
Lectures, discussions, practical exercises, simulation sessions, and activities related to the case study are conducted face-to-face.
Active student participation is expected. Attendance is compulsory for the BESTEST group exercise sessions and the case study briefing.
The compulsory attendance for the BESTEST group exercise and case-study briefing is explicitly stated in the revised course document.
Recommended or required readings
Other site(s) used for course materials
- Google Drive (https://drive.google.com/drive/folders/1-oUotzJvOIeofk7hGxgSPEYQdN5jxZ5Y?usp=drive_li)
Further information:
Other site(s) used for course materials
- Google Drive (https://drive.google.com/drive/folders/1-oUotzJvOIeofk7hGxgSPEYQdN5jxZ5Y?usp=drive_li)
Further information:
Course materials, scientific papers, reference documents, manuals, and resources required forthe exercises will be made available to students through the course platforms.
A list of required scientific readings is provided by the instructor. Students are expected to readthe assigned papers before the corresponding sessions and to be able to discuss their concepts,methods, results, and limitations.
DesignBuilder, EnergyPlus, and BESTEST manuals and technical resources, together withreference documents on building performance simulation, model calibration, and qualityassurance, also form part of the course materials.
Part 1
Other site(s) used for course materials
- G-Drive (https://drive.google.com/drive/folders/1-oUotzJvOIeofk7hGxgSPEYQdN5jxZ5Y?usp=drive_li)
Further information:
Other site(s) used for course materials
- Google Drive (https://drive.google.com/drive/folders/1-oUotzJvOIeofk7hGxgSPEYQdN5jxZ5Y?usp=drive_li)
Further information:
Course materials, scientific papers, reference documents, manuals, and resources required forthe exercises will be made available to students through the course platforms.
A list of required scientific readings is provided by the instructor. Students are expected to readthe assigned papers before the corresponding sessions and to be able to discuss their concepts,methods, results, and limitations.
DesignBuilder, EnergyPlus, and BESTEST manuals and technical resources, together withreference documents on building performance simulation, model calibration, and qualityassurance, also form part of the course materials.
Part 2
Other site(s) used for course materials
- Google Drive (https://drive.google.com/drive/folders/1-oUotzJvOIeofk7hGxgSPEYQdN5jxZ5Y?usp=drive_li)
Further information:
Course materials, scientific papers, reference documents, manuals, and resources required for the exercises will be made available to students through the course platforms.
A list of required scientific readings is provided by the instructor. Students are expected to read the assigned papers before the corresponding sessions and to be able to discuss their concepts, methods, results, and limitations.
DesignBuilder, EnergyPlus, and BESTEST manuals and technical resources, together with reference documents on building performance simulation, model calibration, and quality assurance, also form part of the course materials.
Assessment methods and criteria
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Further information:
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Further information:
Three-hour closed-book written examination: 80% of the final grade
Case study: 20% of the final grade
BESTEST group exercise: Pass/fail
The three-hour closed-book written examination covers the lectures, scientific readings, andconcepts applied in the case study. It comprises nine main questions, from which studentschoose four to answer. The questions assess understanding of the main concepts, theirapplication to building simulation, and the ability to compare and critically analyze differentmodeling approaches and techniques.
The case study is assessed based on the quality of the application of the course principles,including building characterization, monitoring, modeling, quality assurance, calibration,performance analysis, and interpretation of results.
The group exercise focuses on creating and verifying the BESTEST 600 reference case usingDesignBuilder. It is assessed on a pass/fail basis.
Attendance at the case study briefing and the group exercise sessions is compulsory. Studentswith more than two unjustified absences from course sessions may not be admitted to theexamination.
This also matches the detailed grading section, which states that the case study assessesapplication of course principles and that attendance at the case-study briefing and BESTESTexercise is compulsory.
Part 1
Exam(s) in session
Any session
- In-person
written exam
Written work / report
Further information:
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Further information:
Three-hour closed-book written examination: 80% of the final grade
Case study: 20% of the final grade
BESTEST group exercise: Pass/fail
The three-hour closed-book written examination covers the lectures, scientific readings, andconcepts applied in the case study. It comprises nine main questions, from which studentschoose four to answer. The questions assess understanding of the main concepts, theirapplication to building simulation, and the ability to compare and critically analyze differentmodeling approaches and techniques.
The case study is assessed based on the quality of the application of the course principles,including building characterization, monitoring, modeling, quality assurance, calibration,performance analysis, and interpretation of results.
The group exercise focuses on creating and verifying the BESTEST 600 reference case usingDesignBuilder. It is assessed on a pass/fail basis.
Attendance at the case study briefing and the group exercise sessions is compulsory. Studentswith more than two unjustified absences from course sessions may not be admitted to theexamination.
This also matches the detailed grading section, which states that the case study assessesapplication of course principles and that attendance at the case-study briefing and BESTESTexercise is compulsory.
Part 2
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Further information:
Three-hour closed-book written examination: 80% of the final grade
Case study: 20% of the final grade
BESTEST group exercise: Pass/fail
The three-hour closed-book written examination covers the lectures, scientific readings, and concepts applied in the case study. It comprises nine main questions, from which students choose four to answer. The questions assess understanding of the main concepts, their application to building simulation, and the ability to compare and critically analyze different modeling approaches and techniques.
The case study is assessed based on the quality of the application of the course principles, including building characterization, monitoring, modeling, quality assurance, calibration, performance analysis, and interpretation of results.
The group exercise focuses on creating and verifying the BESTEST 600 reference case using DesignBuilder. It is assessed on a pass/fail basis.
Attendance at the case study briefing and the group exercise sessions is compulsory. Students with more than two unjustified absences from course sessions may not be admitted to the examination.
This also matches the detailed grading section, which states that the case study assesses application of course principles and that attendance at the case-study briefing and BESTEST exercise is compulsory.
Work placement(s)
Organizational remarks
Course notes, scientific papers, manuals, exercise instructions, and other supporting materialswill be made available through the course's digital platform.
Students must have a laptop with DesignBuilder installed in order to participate in the practicalexercises and develop their simulation model.
Students are strongly encouraged to select the residential building for their case study at thebeginning of the semester. They must ensure sufficient access to the building and to theinformation required to characterize its geometry, envelope, systems, and energy use.
The monitoring campaign should also start sufficiently early to provide the data required forbuilding characterization and model calibration.
Students are encouraged to ask questions during lectures and to contact the instructor duringoffice hours or by appointment when they encounter difficulties.
Use of generative artificial intelligence
The use of generative artificial intelligence is permitted and encouraged in this course when itsupports the learning objectives. All use must comply with the University of Liège Chartergoverning the use of generative artificial intelligence and with academic integrity requirements.
Generative AI may be used for building modeling and simulation, particularly for the BESTESTexercise and the case study. Students are encouraged to couple AI tools with Python,DesignBuilder, EnergyPlus, or other digital tools to automate selected tasks, generate or modifyinput files, develop scripts, analyze results, detect inconsistencies, and support verification andquality assurance procedures.
Generative AI may also be used for information research, analysis, synthesis, preparation ofpresentations, and the drafting, structuring, and improvement of the report.
However, any contribution made by AI to submitted work must be explicitly disclosed. Studentsmust identify the parts of their work that were produced or modified with AI assistance anddocument the main prompts used, together with the AI tool employed. This documentationmust make it possible to understand how AI contributed to the work and how its outputs wereverified.
AI-generated outputs must be critically verified. Students remain fully responsible for theaccuracy of the information, calculations, code, references, data, assumptions, simulation files,and results they submit. They must be able to explain and justify their model, code,assumptions, inputs, and results independently of their use of AI.
An exception applies to the scientific paper reading exercises. Assigned papers must bepersonally read and analyzed by the students, without using AI as a substitute for reading.After reading the paper, students may use AI to help prepare a summary, structure theiranalysis, or create the PowerPoint material for presentation and classroom discussion. Any suchAI use must also be disclosed.
For case studies, data protection and privacy are the responsibility of the students. Personal,confidential, or identifying information concerning occupants, owners, or buildings must not besubmitted to an AI tool without appropriate authorization. Data must be anonymized wherenecessary before being processed using AI tools.
This privacy requirement also fits the existing case-study instructions, which already requirenames, addresses, meter numbers, and other sensitive information to be anonymized.
Part 1
Course notes, scientific papers, manuals, exercise instructions, and other supporting materialswill be made available through the course's digital platform.
Students must have a laptop with DesignBuilder installed in order to participate in the practicalexercises and develop their simulation model.
Students are strongly encouraged to select the residential building for their case study at thebeginning of the semester. They must ensure sufficient access to the building and to theinformation required to characterize its geometry, envelope, systems, and energy use.
The monitoring campaign should also start sufficiently early to provide the data required forbuilding characterization and model calibration.
Students are encouraged to ask questions during lectures and to contact the instructor duringoffice hours or by appointment when they encounter difficulties.
Use of generative artificial intelligence
The use of generative artificial intelligence is permitted and encouraged in this course when itsupports the learning objectives. All use must comply with the University of Liège Chartergoverning the use of generative artificial intelligence and with academic integrity requirements.
Generative AI may be used for building modeling and simulation, particularly for the BESTESTexercise and the case study. Students are encouraged to couple AI tools with Python,DesignBuilder, EnergyPlus, or other digital tools to automate selected tasks, generate or modifyinput files, develop scripts, analyze results, detect inconsistencies, and support verification andquality assurance procedures.
Generative AI may also be used for information research, analysis, synthesis, preparation ofpresentations, and the drafting, structuring, and improvement of the report.
However, any contribution made by AI to submitted work must be explicitly disclosed. Studentsmust identify the parts of their work that were produced or modified with AI assistance anddocument the main prompts used, together with the AI tool employed. This documentationmust make it possible to understand how AI contributed to the work and how its outputs wereverified.
AI-generated outputs must be critically verified. Students remain fully responsible for theaccuracy of the information, calculations, code, references, data, assumptions, simulation files,and results they submit. They must be able to explain and justify their model, code,assumptions, inputs, and results independently of their use of AI.
An exception applies to the scientific paper reading exercises. Assigned papers must bepersonally read and analyzed by the students, without using AI as a substitute for reading.After reading the paper, students may use AI to help prepare a summary, structure theiranalysis, or create the PowerPoint material for presentation and classroom discussion. Any suchAI use must also be disclosed.
For case studies, data protection and privacy are the responsibility of the students. Personal,confidential, or identifying information concerning occupants, owners, or buildings must not besubmitted to an AI tool without appropriate authorization. Data must be anonymized wherenecessary before being processed using AI tools.
This privacy requirement also fits the existing case-study instructions, which already requirenames, addresses, meter numbers, and other sensitive information to be anonymized.
Part 2
Course notes, scientific papers, manuals, exercise instructions, and other supporting materials will be made available through the course's digital platform.
Students must have a laptop with DesignBuilder installed in order to participate in the practical exercises and develop their simulation model.
Students are strongly encouraged to select the residential building for their case study at the beginning of the semester. They must ensure sufficient access to the building and to the information required to characterize its geometry, envelope, systems, and energy use.
The monitoring campaign should also start sufficiently early to provide the data required for building characterization and model calibration.
Students are encouraged to ask questions during lectures and to contact the instructor during office hours or by appointment when they encounter difficulties.
Use of generative artificial intelligence
The use of generative artificial intelligence is permitted and encouraged in this course when it supports the learning objectives. All use must comply with the University of Liège Charter governing the use of generative artificial intelligence and with academic integrity requirements.
Generative AI may be used for building modeling and simulation, particularly for the BESTEST exercise and the case study. Students are encouraged to couple AI tools with Python, DesignBuilder, EnergyPlus, or other digital tools to automate selected tasks, generate or modify input files, develop scripts, analyze results, detect inconsistencies, and support verification and quality assurance procedures.
Generative AI may also be used for information research, analysis, synthesis, preparation of presentations, and the drafting, structuring, and improvement of the report.
However, any contribution made by AI to submitted work must be explicitly disclosed. Students must identify the parts of their work that were produced or modified with AI assistance and document the main prompts used, together with the AI tool employed. This documentation must make it possible to understand how AI contributed to the work and how its outputs were verified.
AI-generated outputs must be critically verified. Students remain fully responsible for the accuracy of the information, calculations, code, references, data, assumptions, simulation files, and results they submit. They must be able to explain and justify their model, code, assumptions, inputs, and results independently of their use of AI.
An exception applies to the scientific paper reading exercises. Assigned papers must be personally read and analyzed by the students, without using AI as a substitute for reading. After reading the paper, students may use AI to help prepare a summary, structure their analysis, or create the PowerPoint material for presentation and classroom discussion. Any such AI use must also be disclosed.
For case studies, data protection and privacy are the responsibility of the students. Personal, confidential, or identifying information concerning occupants, owners, or buildings must not be submitted to an AI tool without appropriate authorization. Data must be anonymized where necessary before being processed using AI tools.
This privacy requirement also fits the existing case-study instructions, which already require names, addresses, meter numbers, and other sensitive information to be anonymized.
Contacts
Prof. Shady Attia
Professor of Sustainable Architecture & Building Technology
Head of the Sustainable Building Design (SBD) Lab
ArGEnCo Department, Faculty of Applied Sciences
University of Liège
Building B52, Office +0/542
Quartier Polytech 1
Allée de la Découverte 9
4000 Liège, Belgium
Tel.: +32 4 366 91 55
Email: shady.attia@uliege.be
Office hours: Friday, 15:00 to 17:00, or by appointment.
Part 1
Prof. Shady Attia
Professor of Sustainable Architecture & Building Technology
Head of the Sustainable Building Design (SBD) Lab
ArGEnCo Department, Faculty of Applied Sciences
University of Liège
Building B52, Office +0/542
Quartier Polytech 1
Allée de la Découverte 9
4000 Liège, Belgium
Tel.: +32 4 366 91 55
Email: shady.attia@uliege.be
Office hours: Friday, 15:00 to 17:00, or by appointment.
Part 2
Prof. Shady Attia
Professor of Sustainable Architecture & Building Technology
Head of the Sustainable Building Design (SBD) Lab
ArGEnCo Department, Faculty of Applied Sciences
University of Liège
Building B52, Office +0/542
Quartier Polytech 1
Allée de la Découverte 9
4000 Liège, Belgium
Tel.: +32 4 366 91 55
Email: shady.attia@uliege.be
Office hours: Friday, 15:00 to 17:00, or by appointment.