Course Overview
The significant advances in machine learning techniques have provided new opportunities for energy system applications, enabling to solve large-scale decision-making and risk assessment problems as well as efficient real-time operation and control of individual assets or a system of assets. The main focus of this course is to clarify the rationale behind the utilization of machine learning techniques in energy system applications.
Course Highlights
The course provides an overview of relevant machine learning methods and relevant energy system applications from the perspective of both a system operator and individuals, taking into account the physics of assets.
MAIN GOAL
It equips students with tools and skills to effectively prepare data, recognize and employ appropriate machine learning methods for various energy system applications. Subsequently, students will apply these methods to a number of key examples from the viewpoint of both individual stakeholders and a system operator, while evaluating their performance.
Learning Outcomes
After completion of this course, you will gain the ability to:
- + Explain motivations for exploiting machine learning methods for energy system applications, identifying potential opportunities and challenges.
- + Discuss key machine learning concepts and techniques for energy system applications.
- + Apply mathematical tools and algorithms related to machine learning techniques for energy system applications.
- + Interpret how machine learning models can help solve complex problems in energy systems.
- + Develop machine learning models from the perspective of both a system operator and an individual stakeholder.
- + Apply the python programming language for processing data and implementing machine learning models.
- + Analyze the results and evaluate the performance of machine learning methods adopted.
- + Effectively present and discus results from assignments in written form.
Meet Your Instructors
Professor
Researcher
Admissions
Entry Requirements
- + Have completed 02450 Introduction to Machine Learning and Data Mining, or similar.
- + Knowledge of the Python programming language.
- + Basic understanding of energy systems are highly expected.
Teaching and Assessment Methods
- + Lectures
- + Exercises
- + Project work
- + Written examination and reports
- + The assessment is based on group-based reports for 3 assignments and the performance in the written examination
Application Deadline: Visit institution page at the link below.
Fees & Funding
Tuition Fees
Visit institution page for information on fees and application deadlines.
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Course Info
Contact Jalal Kazempour for any additional information relating to this course.
