EnergyAgent is an LLM-powered conversational assistant for household energy monitoring and efficiency. It enables natural-language queries over smart-meter data and provides strictly factual responses derived from recorded consumption. User questions are translated into explicit calculation steps processed by an analytics engine, ensuring numerical accuracy. The system then converts these results into clear, actionable insights to help users better understand and optimize their energy usage.

This project leverages Physics-Informed Neural Networks (PINNs) to model and forecast the thermal performance of power transformers in the Belgian transmission network. By combining physical thermal equations with historical operational and environmental data, the approach enables accurate temperature estimation, early detection of overheating risks, and supports data-driven predictive maintenance strategies.

This project represents a strategic shift from single, general-purpose AI models to an ensemble ecosystem purpose-built for the legal domain. Unlike conventional models that struggle with contractual complexity, this architecture mirrors a legal professional’s reasoning: prioritizing precision, intent comprehension, and rigorous source validation.

The project explores how AI models may prioritize certain information over others in corporate scenarios. From favoring recent data to reinforcing specific perspectives, we analyze these biases to promote more transparent and fair AI systems. Our partners are DTSC and UAntwerpen