Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Wanfu Zheng, Dan Wang and Zhe Wang

Applied Energy, 2024, vol. 374, issue C, No S0306261924013527

Abstract: Model Predictive Control (MPC) has demonstrated its capability to significantly enhance energy efficiency while ensuring indoor comfort. This study focuses on the development and fair comparison of two Economic Model Predictive Control(EMPC) frameworks with the goal of optimizing demand response for utility cost reduction and ensuring thermal comfort in residential buildings. The first framework, known as RC-MPC, utilizes a reduced-order thermal Resistance–Capacitance network model to represent the thermal dynamics of the building. It formulates the optimization problem as a Linear Programming (LP) task, which is solved using CPLEX. The second framework, known as ANN-MPC, employs an Artificial Neural Network model capture the thermal behavior of the building, It addresses a non-convex optimization problem through projected stochastic gradient descent. To evaluate the performance of these frameworks, experiments are conducted using a single-zone dwelling within the BOPTEST framework. The results demonstrate that both approaches effectively reduce operational costs and enhance comfort, achieving comparable performance and outperforming the baseline Rule-Based Control (RBC) method. The discomfort reductions range from 30% to 95%, and energy expense savings range from 17% to 34%. Furthermore, the study examines the impact of various MPC hyperparameters. The findings highlight that a proper control horizon and formulating the objective function accurately are crucial for achieving optimal control performance. Additionally, tightening the comfort range is found to be important in managing the comfort violations, and the choice of solver can influence both the control performance and computational efficiency.

Keywords: Model predictive control; Grey-box model; Artificial neural network; Building energy optimization; BOPTEST (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924013527
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013527

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123969

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().