Thesis Work - Identifying distributed energy resources from consumption patterns
Core
Develop and evaluate machine learning and statistical models to identify distributed energy resources (e.g., EVs, heat pumps, solar PV) from electricity consumption data using load disaggregation techniques.
Role type
Master's thesis student (Data Science/Machine Learning)
Builds
Data-driven methods and AI-powered solutions for customer and energy-system domains
Domain
Energy systems, electrification, and the energy transition
Deliverable
production ML models
Required skills
statistical modelling, machine learning, Python, time-series analysis, predictive modelling
Preferred skills
knowledge of energy systems, interest in load forecasting and flexibility markets
Technologies
Python, data science libraries
Responsibilities
Review literature on load disaggregation; develop and evaluate ML/statistical models; assess model performance and uncertainty; provide recommendations for implementation
Seniority
Master's student (30 ECTS credits)