A scenario-based stochastic MPC framework for coordinated HVAC and battery control in residential buildings

Maintaining thermal comfort while reducing residential energy costs is challenging under uncertain weather conditions and increasing renewable energy penetration. This paper proposes a scenario-based stochastic Model Predictive Control (MPC) framework for the coordinated operation of HVAC systems and battery energy storage. A validated 2R2C thermal model is integrated into the optimization process and calibrated using EnergyPlus simulations and measured residential data. Forecast uncertainty in ambient temperature and solar irradiance is represented through Monte Carlo scenario generation and scenario reduction. The controller minimizes electricity costs while enforcing thermal comfort constraints derived from ASHRAE Standard 55. Performance is evaluated across five Mediterranean climates using benchmark comparisons, ablation studies, robustness testing, and uncertainty quantification. Results show annual electricity cost reductions of 14–20% compared with conventional control strategies while maintaining comfort violations below 2%. Additional analyses demonstrate robust performance under extreme weather conditions and confirm the effectiveness of combining stochastic optimization, thermal modeling, and battery storage for residential energy flexibility. Practical Application This study provides building engineers and energy managers with a practical framework for coordinating residential HVAC systems, photovoltaic generation, and battery storage under uncertain weather conditions. Across five Mediterranean climates, the proposed controller achieves 14–20% annual electricity cost savings compared with conventional control, while keeping thermal comfort violations below 2%. Its reduced-order thermal model and scenario reduction approach support computationally efficient implementation in residential energy management systems. Practical deployment requires building-specific calibration, periodic forecast updates, and appropriate fallback controls to maintain safe and reliable operation.