Electrical Engineering - Data science for energy consumers
Work description
The use of causality models to evaluate the price elasticity of residential consumers has grown with the need to allow a better understanding of the flexibility of energy consumption and its optimization. By generating synthetic data, it is possible to simulate consumer behavior in response to different tariffs, allowing for a more accurate assessment of their reaction to price variations. However, creating and integrating this data into analysis tools is not a trivial task, and it is necessary to develop models that generate data efficiently and close to reality. The work expected in this area, in relation to the description given, is as follows: - Study existing models in the literature on causality and price elasticity to simulate the behavior of residential consumers; - Formulate a problem for generating synthetic data and evaluating price elasticity; - Develop and implement a synthetic data generation module in a Python environment; - Integrate the synthetic data model with the existing tool and test its performance; - Prepare a scientific report on the activities and write scientific articles.
Academic Qualifications
Electrical engineering, computer science, applied mathematics, computer science or similar
Minimum profile required
- Basic knowledge of data analysis;- Basic knowledge of energy systems;- Knowledge of the Python programming language;- Fluency in English (written and spoken);
Preference factors
-Interest or basic experience in causal models and data analysis. -Interest or experience in generating synthetic data and modeling consumer behavior; -Experience in scientific research activities; -Programming experience in Python;
Application Period
Since 29 May 2025 to 16 Jun 2025
Centre
Power and Energy Systems