| Code |
18169
|
| Year |
2
|
| Semester |
S1
|
| ECTS Credits |
6
|
| Workload |
PL(30H)/TP(30H)
|
| Scientific area |
MECÂNICA COMPUTACIONAL
|
|
Entry requirements |
N.A.
|
|
Learning outcomes |
The learning objectives include the knowledge, skills, and competencies to be developed by students in main issues in thermal systems design and simulation 2, with particular emphasis on efficient and intelligent buildings:Understand Fundamentals of Energy Efficiency: Analyze and apply energy efficiency principles, including thermal insulation, ventilation, and natural lighting
Implement Automation Solutions: Design and configure automation systems that integrate sensors, actuators, and communication protocols
Integrate Renewable Energy and Energy Management: Identify and implement solutions that combine renewable energy sources and storage systems, for energy consumption management
Utilize Artificial Intelligence and Big Data: Analyze data and use artificial intelligence to predict operational parameters and improve building efficiency
Apply Computational Tools: Evaluate thermal performance and energy efficiency in buildings using computational analysis tools
|
|
Syllabus |
1. Introduction to Efficient and Intelligent Buildings
-Concept and importance of efficient and intelligent buildings
-Examples of model buildings
-Technological trends and challenges for emerging technologies
-Standards and regulations
2. Energy Efficiency Concepts
-Principles of energy efficiency
-Energy consumption analysis
-Energy optimization strategies in building envelopes
-Energy efficiency indicators
3. Building Automation Systems
-Automation and control technologies
-Sensors and actuators: types and applications
-Communication protocols
-Monitoring and control of building systems
4. Renewable Energy and Energy Management
-Renewable energies in buildings
-Integration of distributed generation systems
-Energy management and storage
-Consumption optimization
5. Integration of AI and Big Data
-Data usage in intelligent buildings
-AI for building management and operational forecasting
-Optimization algorithms
|
|
Main Bibliography |
-Santamouris, M. (2013). Energy efficiency in buildings: Theory and practice. Routledge.
-Agger, J. P. (2018). Building science: Concepts and applications in environmental performance and energy efficiency. Wiley.
-Desideri, U., & Asdrubali, F. (Eds.). (2020). Handbook of energy efficiency in buildings: A life cycle approach. Springer.
-Sinopoli, J. (2016). Smart buildings systems for architects, owners, and builders (2nd ed.). Butterworth-Heinemann.
-Lawless, W. F., Mittu, R., & Sofge, D. (Eds.). (2019). Artificial intelligence for the Internet of everything. Academic Press.
-Jadhav, N. Y. (2016). Green and smart buildings: Advanced technology options. Springer. https://doi.org/10.1007/978-981-10-1002-6
-Scientific papers in Scopus and Web of Science
|
|
Teaching Methodologies and Assessment Criteria |
The assessment is conducted in two stages, each covering different areas of knowledge:
Analysis and Synthesis Work (TAS, 30%): Bibliographic research on a specific topic from the syllabus, with regular presentations throughout the semester, resulting in a final report and oral discussion.
Project (PRO, 70%): Computational modelling, including a report and oral discussion.
Final Grade (CF): CF = TAS + PRO
Requirements:
CF >= 10 points.
TAS >= 6 points.
PRO >= 6 points.
Grade improvement requires a final exam, maintaining the same criteria and CF calculation
|
|
Language |
Portuguese. Tutorial support is available in English.
|