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Research Article

In search of optimal building behavior models for model predictive control in the context of flexibility

Arash Erfani1( ), Tohid Jafarinejad1, Staf Roels1, Dirk Saelens1,2
KU Leuven, Department of Civil Engineering, Building Physics and Sustainable Design Section, Kasteelpark Arenberg 40 Bus 2447, 3001 Heverlee, Belgium
EnergyVille, Thor Park 8310, Genk 3600, Belgium
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Abstract

Model predictive control (MPC) is an advanced control technique. It has been deployed to harness the energy flexibility of a building. MPC requires a dynamic model of the building to achieve such an objective. However, developing a suitable predictive model is the main challenge in MPC implementation for flexibility activation. This study focuses on the application of key performance indicators (KPIs) to evaluate the suitability of MPC models via feature selection. To this end, multiple models were developed for two houses. A feature selection method was developed to select an appropriate feature space to train the models. These predictive models were then quantified based on one-step ahead prediction error (OSPE), a standard KPI used in multiple studies, and a less-often KPI: multi-step ahead prediction error (MSPE). An MPC workflow was designed where different models can serve as the predictive model. Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation. Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.

Keywords

smart homebuilding flexibilitymodel predictive control for buildingsbuilding behaviour identification

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Building Simulation
Volume 17 Issue 1,
January 2024
Pages 71-91
DOI: 10.1007/s12273-023-1079-0
Cite this article:
Erfani A, Jafarinejad T, Roels S, et al. In search of optimal building behavior models for model predictive control in the context of flexibility. Building Simulation, 2024, 17(1): 71-91. https://doi.org/10.1007/s12273-023-1079-0

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Received: 14 July 2023
Revised: 29 August 2023
Accepted: 17 September 2023
Published: 14 October 2023
© Tsinghua University Press 2023
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