Validation Framework for Model Predictive Control in Residential Buildings: An EnergyPlus–Python Co-Simulation Approach

Authors

  • Maricielo Centeno Padilla Universidad Tecnológica del Perú UTP - (PE), Perú
  • Andersson Chavez Ortiz Universidad Tecnológica del Perú UTP - (PE), Perú
  • Alert Mendoza Acosta Universidad Tecnológica del Perú UTP - (PE), Perú

DOI:

https://doi.org/10.18687/LACCEI2026.1.1.1945

Keywords:

Model Predictive Control, Building Energy Management, Simulation-Based Validation, EnergyPlus–Python Co-Simulation, Residential Buildings.

Abstract

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.

Downloads

Published

2026-07-27

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

LACCEI retains copyright of all published articles under the terms of its copyright transfer agreement. As the copyright holder, LACCEI distributes the articles to the public under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

How to Cite

Centeno Padilla, M., Chavez Ortiz, A., & Mendoza Acosta, A. (2026). Validation Framework for Model Predictive Control in Residential Buildings: An EnergyPlus–Python Co-Simulation Approach. LACCEI, 1(14). https://doi.org/10.18687/LACCEI2026.1.1.1945

Most read articles by the same author(s)