A large amount of energy is consumed to create comfortable built environments for people, associated with a large amount of carbon emissions. New methods and tools are needed to increase the use of renewable energy in cities. As energy systems in buildings, districts and cities are complex, energy saving and energy flexibility provision must reflect system physics for efficient energy management. Thus, this project integrates system physics in a hierarchical spatial and temporal framework, including models for energy aggregation at different levels, combining machine learning with mathematical models of known physical processes. This project aims to develop innovative smart energy management technologies and solutions for buildings and districts in cities by effectively leveraging advanced big data and mathematical modelling technologies. These include data-driven predictive models for energy dynamics characterization and energy flexibility prediction for buildings and districts; model predictive control and optimization methods for distributed energy systems; and game theory-based methods for optimization and coordination of buildings and distributed energy systems. Methods developed will be tested and showcased in living labs.
Responsibilities and qualifications
The overall focus of the project will be data analytics, data-driven modeling, and energy flexibility characterization. Your work will involve multiple techniques in the fields of both building technologies and mathematical modeling. The tasks include data-driven characterization of energy dynamics of typical energy systems in buildings and districts; aggregation and disaggregation of energy consumption and flexibility; and model predictive control of HVAC systems.
You need the following specific qualifications
- Knowledge of HVAC system operation in buildings
- Skills of big data analysis and data-driven modeling
- A background in mechanical engineering, civil engineering, electrical engineering, or other relevant fields, and experience in building automation or building energy management is preferred
- Candidates with knowledge of time series analysis are preferred
- Proven experience with programming, e.g., Modelica, Python, R
Besides, you will take part in our teaching of BSc and MSc students in student projects and/or in ordinary courses as Teaching Assistant.
You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree.
To apply, please read the full job advertisement, by clicking the ‘Apply’ button.
Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.