Course syllabus - Smart Building and Smart Cities

Objectives

The course provides foundational and applied knowledge of smart buildings and smart cities, focusing on smart technologies, control systems, energy flexibility, and cybersecurity. A particular emphasis is placed on the Smart Readiness Indicator (SRI) as a strategic tool under the EU’s green transition policy. The course equips students and professionals with interdisciplinary skills to implement and evaluate smart solutions for sustainable urban development.

Learning outcomes

Upon completion of the course, students will be able to:

1. Explain and critically evaluate the concepts, characteristics, and sustainability roles of smart buildings
2. Describe and assess the Smart Readiness Indicator (SRI) framework and other key performance indicators used for evaluating building smartness.
3. Design, implement, and evaluate building control strategies that balance energy efficiency, comfort, and operational resilience.
4. Identify and mitigate cybersecurity and ethical challenges in smart building infrastructures, reflecting on privacy and data governance.
5. Integrate and critically reflect on how digital tools and communication systems enable interaction between individual buildings and larger building clusters or urban energy networks.

Course content

The course is organised into two interconnected modules:

Module 1 provides a theoretical and conceptual grounding in the field. Students are introduced to the definitions, characteristics, and technologies that constitute smart buildings, and to their relevance for sustainable development. Central to this module is the study of the Smart Readiness Indicator (SRI) and other frameworks used to evaluate the intelligence and performance of buildings.

Module 2 focuses on the application of control theory, automation, and data-driven optimisation in building operation. Students explore how sensors, actuators, and feedback systems can be used to manage and improve comfort, energy efficiency, and operational performance in buildings. Through case studies and simulation exercises, they apply digital tools to analyse and optimise smart building functions. The module further addresses issues of cybersecurity, system resilience, and ethical data management, as well as the integration of smart buildings into larger networks such as building clusters and urban energy systems

Specific requirements

7.5 credits in building and energy systems, 5 credits Computer Programming with Python and at least 3,5 credits Big data and Machine Learning on Cloud Platform for Industrial Applications or equivalent. In addition Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

TEN1, Written examination, 2.5 credits, grade: Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient (F). Learning outcome 1-2, and 4.

PRO1, Project report, 3.5 credits, grade Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F). Learning outcome 2-5.

SEM1, Seminar, 1.5 credits, grade: Pass (G) or Fail (U). Learning outcome 4-5.

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

AF-skala