In the power industry, traditional centralized networks are giving way to distributed ones (smart grids). Consumers are becoming “prosumers”, as they can also produce electricity, and energy sources are distributed across the grid. New high capacity energy storage systems have become a part of the grid, and plug-in electric vehicles have emerged. Traditional methods are not well suited to these new trends, so new approaches based on intelligent data analysis are needed. Our research aims to improve methods for predicting consumption and production and optimizing smart grids using machine learning and statistical analysis.
We have also developed applications to save energy and costs, to help power customers better understand their bills and adjust their energy consumption accordingly, and to assist electric vehicle drivers. To carry out extensive experiments, we have accumulated datasets of interest and tutorials, which are available here. The team also has experience in other areas, such as cancer treatment research, where large datasets need to be processed to obtain useful information.
RESEARCH AREAS OF INTEREST:
- Predictive modelling (energy consumption and production)
- Clustering (customer segmentation)
- Anomaly detection
- Smart grid optimization