This course provides students with knowledge of advanced technologies and explores their potential in realizing smart buildings. It focuses on how these cutting-edge technologies can enhance building energy performance and indoor environmental quality. It primarily consists of four parts. In the first part, students will explore diverse modelling approaches for building, system, and disturbance (including occupant) modelling. The course will delve into forward (physics-based), inverse (data-driven), and hybrid modelling approaches. The second part of the course centers around the optimal control of building energy systems. Students will gain a deep understanding of two widely used techniques: model predictive control and reinforcement learning. The third part introduces students to modern methods for automated fault detection and diagnostics. Key topics include the concept of digital twinning, which allows for the virtual representation of physical systems, and anomaly detection methods. In the last part of the course, students will focus on sensors and user interfaces. Throughout the course, students will engage in hands-on exercises to enhance their practical skills and gain valuable experience. They will have the opportunity to implement and test different technologies in virtual environments using computer emulators and physical mock-up models developed with Raspberry Pi.