MABY4600 Digital Twin-Driven AI for Sustainable Building Design Course description

The rapid advancement of digital twins, machine learning, and optimization methods is transforming sustainable building design. These technologies enable precise prediction and analysis of energy consumption, occupant comfort levels such as Predicted Percentage of Dissatisfied (PPD), and environmental impacts. Tools such as Life Cycle Assessment (LCA) provide critical insights into the long-term sustainability of building materials and design choices. Genetic Algorithm (GA) optimization identifies solutions that balance energy efficiency, comfort, and environmental performance. These innovations address the demands of climate adaptation and resource efficiency, fostering robust and sustainable design strategies.

This course aims to equip students with the knowledge and skills necessary to address complex challenges in designing energy-efficient, climate-resilient, and sustainable building envelopes. The course emphasizes the integration of advanced technologies with traditional building physics principles, exploring the interaction between materials, components, and environmental impacts. Building on foundational insights from MABY4200 Building Physics and Climate Adaptation of Buildings and MABY4700 Life Cycle Assessment for Built Environment, the course combines theoretical frameworks with practical applications. Students will learn to use digital twins, machine learning, and optimization methods alongside sustainability assessments and life cycle analyses to create innovative and efficient building solutions. The following topics are addressed in particular:

• Digital twin technology for predicting and optimizing energy performance and occupant comfort.
• Machine learning for forecasting energy use, occupant discomfort (e.g., PPD), and environmental impacts.
• Genetic algorithm (GA) optimization to balance energy efficiency, comfort, and sustainability in building design.
• Life cycle assessment (LCA) for evaluating environmental impacts and improving design decisions.
• Relevant standards and regulations.
• Principles of zero emission buildings, passive- and plus-house building design.
• Integration of building physics principles in the holistic design of building envelopes.
• Thermal storage in conventional and innovative building materials (e.g., PCMs).
• Dynamic building energy simulations.

MABY4200 Building Physics and Climate Adaptation of Buildings.

MABY4700 Life Cycle Assessment for Built Environment.

Or courses with an equivalent learning outcome.

None.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge:

The student:

• understands the application of digital twins, machine learning, and optimization techniques in sustainable building design.
• has advanced knowledge of building physics, climate impacts, and building materials to propose robust and innovative solutions.
• can evaluate climate adaptation solutions for building envelopes and components.
• has insight into the physical and thermal properties of building materials and their life cycle impacts.
• is knowledgeable about embodied and operational emissions from buildings and strategies to reduce them.
• understands the integration of sustainability principles and advanced tools to create environmentally sound building designs.

Skills:

The student is capable of:

• Applying digital twins and machine learning to predict energy performance and occupant comfort.
• using LCA and GA optimization to enhance design sustainability and efficiency.
• analyzing and justifying choices for materials and components based on building physics calculations and life-cycle assessments.
• designing comprehensive sustainable building envelopes with detailed descriptions of materials and components.
• interpreting simulation tool results to improve and optimize designs.
• assessing the condition and maintenance needs of materials and components in existing buildings.

General competence:

The student is capable of:

• keeping up with the latest advancements through scholarly research.
• collaborating effectively in teams to address complex design challenges.
• presenting findings and designs in a professional and scholarly manner through written reports.

The teaching will largely consist of digital and physical lectures, software demonstration and exercises. Students will also be given a major project assignment in which they are to design an sustainable building with regards to building physics, climate adaptation, energy efficiency and indoor environment as well as CO2 emissions.

Digital lectures will be recorded, and the material will be made available to students on CANVAS.

Each student must complete and pass an individual knowledge test with a minimum score of 75% in order to be eligible to submit the final project.

If this requirement is not met, the student will be granted one opportunity to retake the test by a specified deadline.

Project report prepared in groups of 2 students, approx. 80 - 100 pages (excl. appendices).

The exam can be appealed.

All aids permitted.

Grade scale A-F.

Two internal examiner. (External examiners are used regulary).

Haidar Hosamo