Energy Engineering

The course provides an overview of the fundamental principles, technologies, and design strategies of Smart Cities, integrating theoretical lectures with practical exercises using Python. The topics include: - Introduction to the concept of Smart City: definitions, key features, and main challenges - Introduction to the use of Python for urban data analysis: basic tools and essential libraries - Urban data analysis: sources, methods, and tools for data interpretation and visualization - Energy in smart cities: smart grids, energy management, and integration of renewable sources - Smart mobility: sustainable urban transport systems and shared mobility models - Environmental management and monitoring of air, water, and soil quality - Urban design and sustainability: building energy efficiency and intelligent resource management - Governance, citizen participation, and digital platforms for urban management - Emerging technologies: artificial intelligence, digital twins, and urban computing applied to the urban context

Students are expected to have a basic knowledge of mathematics and statistics, with particular emphasis on the ability to calculate means, variances, and standard deviations, an understanding of the fundamental concepts of data distribution, and the ability to interpret graphical representations such as histograms and scatter plots. In addition, a knowledge of key concepts in Technical Physics is required, particularly the fundamental principles of heat transfer, including conduction, convection, and radiation, as well as familiarity with the concept of energy balance and an understanding of the basic concepts related to solar radiation.

Scientific papers sourced from the literature will be used.

Attendance is not mandatory, but it is strongly recommended.

The assessment of the students' acquired knowledge will be structured as follows: Ongoing Assessment (30%) During the course, students will present their project, objectives, and progress. The assessment will consider the participation of all group members and the feedback provided to other groups. Final Project (70%) Students will develop a group project related to the topics covered during the course. The project will be evaluated based on technical accuracy, creativity in the proposed solution, and clarity of presentation.

Teaching activities will be conducted primarily in person, alternating between theoretical lectures and practical sessions focused on the application of concepts through the use of Python. On specific occasions, such as seminars with external speakers, online delivery methods may be adopted.

The course provides an overview of the fundamental principles, technologies, and design strategies of Smart Cities, integrating theoretical lectures with practical exercises using Python. The topics include: - Introduction to the concept of Smart City: definitions, key features, and main challenges - Introduction to the use of Python for urban data analysis: basic tools and essential libraries - Urban data analysis: sources, methods, and tools for data interpretation and visualization - Energy in smart cities: smart grids, energy management, and integration of renewable sources - Smart mobility: sustainable urban transport systems and shared mobility models - Environmental management and monitoring of air, water, and soil quality - Urban design and sustainability: building energy efficiency and intelligent resource management - Governance, citizen participation, and digital platforms for urban management - Emerging technologies: artificial intelligence, digital twins, and urban computing applied to the urban context

Students are expected to have a basic knowledge of mathematics and statistics, with particular emphasis on the ability to calculate means, variances, and standard deviations, an understanding of the fundamental concepts of data distribution, and the ability to interpret graphical representations such as histograms and scatter plots. In addition, a knowledge of key concepts in Technical Physics is required, particularly the fundamental principles of heat transfer, including conduction, convection, and radiation, as well as familiarity with the concept of energy balance and an understanding of the basic concepts related to solar radiation.

Scientific papers sourced from the literature will be used.

Attendance is not mandatory, but it is strongly recommended.

The assessment of the students' acquired knowledge will be structured as follows: Ongoing Assessment (30%) During the course, students will present their project, objectives, and progress. The assessment will consider the participation of all group members and the feedback provided to other groups. Final Project (70%) Students will develop a group project related to the topics covered during the course. The project will be evaluated based on technical accuracy, creativity in the proposed solution, and clarity of presentation.

Teaching activities will be conducted primarily in person, alternating between theoretical lectures and practical sessions focused on the application of concepts through the use of Python. On specific occasions, such as seminars with external speakers, online delivery methods may be adopted.