Adaptive Machine Learning for Automatic Load Optimization in Connected Smart Green Townhouses

This paper presents an adaptive Machine Learning (ML)-based framework for automatic load optimization in Connected Smart Green Townhouses (CSGTs) The system dynamically optimizes load consumption and transitions between grid-connected and island modes. Automatic mode transitions reduce the need for manual changes, ensuring reliable operation. Actual occupancy, load demand, weather, and energy price data are used to manage loads which improves efficiency, cost savings, and sustainability. An adaptive framework is employed that combines data processing and ML. A hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) model is used to analyze time series and spatial data. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to balance costs, carbon emissions, and efficiency. The results obtained show a 3–5% improvement in efficiency for grid-connected mode and 10–12% for island mode, as well as a 4–6% reduction in carbon emissions.