Smart Energy‐Water Synergy in Green Buildings: Adaptive Ground‐Source Heat Pump System and Renewable Energy Resources Integration Toward Climate‐Resilient

This study investigates the combination of a ground‐source heat pump (GHP) with a hybrid solar‐geothermal energy system and a machine learning (ML)‐based smart water‐usage monitoring module to improve resource efficiency in green buildings. A random forest (RF) model was trained in MATLAB/Simulink using historical water‐use data and environmental variables to forecast short‐term cooling‐related water demand and to drive prediction‐based operational scheduling (i.e., supervisory adjustments of cooling‐cycle timing and water‐flow operation in the simulation) while maintaining indoor comfort. Simulations were performed for peak summer conditions in Binzhou, China, and Riyadh, Saudi Arabia. In Binzhou, the proposed hybrid system reduced electricity consumption by 31.24% and decreased the cooling water footprint by 19.56% compared with a conventional baseline. The smart water‐management plan in Riyadh cut down on the amount of cooling water used by a lot (up to ~50% on days when demand was high) and lowered the water footprint by 21.20%. These main findings showed that the potential of merging renewable energy combination with data‐driven (DD) resource monitoring led into the lower energy and water footprints across contrasting climatic conditions.