Conference Agenda

Buildings account for a significant share of global energy consumption, motivating the development of advanced control strategies aimed at improving energy efficiency while maintaining acceptable thermal comfort levels. In this context, Model Predictive Control (MPC) has been widely investigated for HVAC energy management in buildings. However, despite extensive theoretical research, a persistent gap remains between MPC developments and their practical adoption due to the absence of systematic, engineering-oriented validation procedures prior to physical deployment.

To address this limitation, this paper proposes a simulation-based validation framework for predictive energy control in residential buildings. The framework integrates EnergyPlus, a validated whole-building energy simulation engine, with a Python-based MPC implementation through a co-simulation architecture. It follows the VDI 2206 systems engineering methodology to ensure traceability from requirements definition to system design, integration, and validation.

The applicability of the framework is demonstrated through a case study involving a residential building model representative of tropical coastal climatic conditions in Lima, Peru. An MPC-based HVAC control strategy is evaluated against a conventional proportional–integral (PI) controller under identical operating conditions. Performance is assessed using indicators including HVAC energy consumption, thermal comfort deviation, control stability, and computational response time.

Simulation results indicate improved thermal stability, smoother control behavior, and reduced energy consumption compared to the PI baseline while maintaining acceptable comfort levels. Rather than optimizing a specific controller design, the main contribution of this work lies in defining a structured, engineering-oriented validation framework that supports informed decision-making and reduces implementation risks in building energy management.