Building Performance Simulation and AI Optimization

Content:

Given the increasing complexity of energy and environmental challenges that the building sector is facing, Building Performance Simulation and AI Optimization (BPS-AIO) is an effective tool for supporting the design and operation of high-performance buildings—such as (nearly or net) zero-energy buildings (n/NEBs) in zero-emission neighborhoods (ZENs) and positive energy districts (PEDs) in smart cities.

BPS-AIO combines dynamic energy simulation with computational modeling and draws upon disciplines such as heat transfer, thermodynamics, fluid mechanics, lighting, building technology, thermal and visual comfort, numerical methods, environmental science, and human behavior.

The course is organized into two parts:

• General Part: Covers basic modeling issues and energy simulation of buildings and systems, with emphasis on climate analysis and adaptation strategies.

• Specialization Part:

  • Branch A - Solar Building Design: Modeling solar active (BIPV, BAPV, PVSDs) and passive strategies (daylight metrics, shading devices).
  • Branch B - Advanced HVAC Modeling: Covers HVAC systems, physical-based modeling, and airflow methods (room model, zonal, CFD).

Students will explore how simulation and AI optimization can address real-world challenges such as retrofitting existing buildings, designing for Nordic climate conditions, and contributing to EU climate and energy directives.

Students will gain hands-on experience with industry-standard tools such as IDA ICE/EnergyPlus, Rhino, and Grasshopper as well as possible optimization and life cycle assessment frameworks. In addition, AI techniques like genetic algorithms and parametric modeling will be introduced to enhance simulation workflows.

The course bridges civil engineering, environmental science, and computational design, preparing students to work across disciplines in both research and industry.

Knowledge:

• Understand principles of BPS and AI optimization
• Grasp climate modeling and future climate scenarios using synthetic weather data
• Comprehend theoretical models behind BPS software
• Manage assumptions and limitations in BPS tools
• Illustrate and compare building energy performance
• Design advanced buildings for future challenges

Skills:

• Select appropriate BPS software
• Conduct climate analysis with synthetic datasets
• Create energy models using suitable methods
• Control simulation reliability
• Assess building performance
• Apply simulation results in design, retrofitting, and management
• Gain hands-on experience with BPS and AI optimization tools

General Competence:

• Understand the background and scope of BPS
• Comprehend comfort and indoor air quality in relation to energy use
• Analyze building envelope behavior under dynamic conditions
• Integrate passive and renewable strategies
• Evaluate building resiliency against climate change

Students will be introduced to AI techniques such as genetic algorithms and parametric modeling to enhance simulation workflows.

Learning Methods and Activities

• Lectures
• Exercises
• Simulation-based workshops
• Semester project

All activities are conducted in English. All compulsory activities must be approved to take the exam.

Flexible Attendance Option

While the course is primarily taught on campus in Trondheim, online attendance may be permitted upon request and approval. This option is designed to support international students, remote learners, and professionals seeking flexible access to advanced training in building performance simulation and AI optimization. Please contact the course coordinator for details.

Guest lectures from industry experts

The course may include guest lectures from industry experts and case studies based on real building projects in Norway and Europe.