Towards an AI-enhanced virtual lab for education and training in home energy efficiency and demand management

The rapid increase in global energy demand, especially within the residential sector, has heightened pressure on power grids, raised electricity costs, and intensified environmental concerns. Home Energy Management Systems (HEMSs) have become vital tools for optimising household energy consumption and supporting the shift towards more sustainable and efficient energy systems. Nonetheless, delivering specialised education and training in this area remains a challenge due to the high costs, time requirements, and infrastructure needed to develop practical learning tools. This study introduces an Artificial Intelligence (AI)-enhanced Virtual Lab (VL) as a practical and interactive platform for learning about HEMSs and energy demand forecasting. By simulating realistic household energy scenarios, the VL allows users to apply Demand Management (DM) and Demand Response (DR) strategies with priority rule-based and fuzzy logic controllers, as well as integrate renewable energy sources. Users can analyse energy consumption data, implement optimisation strategies, and train machine learning models to forecast energy demand. Results from the simulation show notable reductions in energy usage and costs, demonstrating the VL's effectiveness in boosting energy efficiency and educational outcomes. By connecting theoretical knowledge with practical application, the AI-powered VL improves learner engagement, supports informed decision-making, and fosters sustainable energy practices, providing a scalable solution for addressing educational and environmental challenges in modern home energy management.