Thesis Code: 20021

Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent

Research Area: ICT for Energy


  • Knowledge of Python
  • Software development skills
  • Basic concepts on data science, signal processing, and machine learning, supervised and unsupervised learning

As defined by Wikipedia and many research articles, Non-Intrusive Load Monitoring (NILM)  or Appliance Recognition software are algorithms that detect changes in the electrical values (power, current, voltage) going into a building to infer what appliances are used in the building as well as their individual energy consumption.  The key advantage of NILM algorithms is the ability to infer and breakdown energy consumption with one meter rather than clipping current transformers to each circuit to monitor. This aims at reducing the installation complexity and cost. Nevertheless, Commercial and industrial facilities have many dynamic loads with a different mode of operations that makes almost impossible to capture signatures.

The objective of this thesis consists in the study of NILM applications for Industrial Applications starting from a deep review of the current state of the art and with the implementation of a machine learning algorithms useful for disaggregating industrial loads. The proposed algorithms will be trained using open and/or synthetic dataset. The candidate will have both the task of studying and evaluating the best algorithms to apply for the case of study.

The candidate is required to implement machine learning algorithms, with the exploratory possibility of deep learning algorithms using popular frameworks (TensorFlow, PyTorch, Keras, etc..).

Main foreseen activities

Contact: send a resume with attached the list of exams to and specifying the thesis code and title.