DANTE

Project Description

The project aims to build an intelligent digital platform to model, control and optimise complex energy assets. The platform integrates advanced technologies such as Digital Twin, Artificial Intelligence and Machine Learning, with the goal of making energy management more efficient, predictive and resilient.

The system is designed to operate in real-world, high-complexity contexts, where the variability of energy flows and the need for real-time response require intelligent and adaptive approaches.

LINKS Foundation Activities

LINKS Foundation is an executing entity of the project and leads the following key activities:

Energy and Comfort Optimisation

Development of systems to optimise thermal comfort using Multi-Agent Reinforcement Learning (MARL) techniques to monitor and manage HVACs systems. The objective is to simultaneously optimise:

• occupant comfort within buildings
• overall energy consumption

The work builds upon thermal models developed within the projects: these are rendered compatible with the Reinforcement Learning framework to perform simulations and to ensure proper training for the developed model.

Catastrophic Event Detection

Development of systems to evaluate the risk and impact catastrophic winter events, such as avalanches, on hydro-electic infrastructures in Alpine areas.

The task leverages satellite imagery to identify, classify and evaluate direct and indirect effects of these phenomena on plant productivity, using computer vision and deep learning algorithms.

Funding & Dates

SDGs — Sustainable Development Goals

🌍 Goal 7 — Affordable and Clean Energy

Key Technologies

⚡ Digital Twin

A real-time digital replica of physical energy assets, enabling simulation, monitoring and advanced control without direct intervention on the real infrastructure.

🤖 Multi-Agent Reinforcement Learning (MARL)

A machine learning approach in which autonomous agents interact with the energy environment to find optimal control strategies that balance comfort and consumption.

🛰️ Satellite Imagery Analysis

Use of remote sensing images and computer vision models to monitor the territory and detect catastrophic natural events such as avalanches in advance, with direct implications for the energy security of mountain infrastructures.

🧠 Machine Learning & AI

Integration of advanced predictive models to anticipate future states of the energy system and act proactively, reducing inefficiencies and operational risks.

Project Partners