AI-driven and optimised infrastructure operation with reinforcement learning & forecasting models

This white paper describes how modern AI technologies – in particular reinforcement learning (Dreamer V3), LSTM-based forecasting models and rule-based automation – are combined in the Eliona platform to create a self-learning, adaptive building management system. The integration of historical and live data creates a digital image of the infrastructure, in which virtual agents simulate optimal control strategies and put them directly into practice. Supplemented by forecasting apps and a powerful rule engine, the result is a closed, AI-orchestrated system that saves energy, smooths peak loads and complies with comfort specifications – without manual intervention.

The vision: an AI agent that can be instructed via natural language to optimise energy consumption autonomously and proactively.