Reinforced model predictive control (RL-MPC) for building energy management

Javier Arroyo, Carlo Manna, Fred Spiessens and Lieve Helsen

Applied Energy, 2022, vol. 309, issue C, No S0306261921015932

Abstract: Buildings need advanced control for the efficient and climate-neutral use of their energy systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two powerful control techniques that have been extensively investigated in the literature for their application to building energy management. These methods show complementary qualities in terms of constraint satisfaction, computational demand, adaptability, and intelligibility, but usually a choice is made between both approaches. This paper compares both control approaches and proposes a novel algorithm called reinforced predictive control (RL-MPC) that merges their relative merits. First, the complementarity between RL and MPC is emphasized on a conceptual level by commenting on the main aspects of each method. Second, the RL-MPC algorithm is described that effectively combines features from each approach, namely state estimation, dynamic optimization, and learning. Finally, MPC, RL, and RL-MPC are implemented and evaluated in BOPTEST, a standardized simulation framework for the assessment of advanced control algorithms in buildings. The results indicate that pure RL cannot provide constraint satisfaction when using a control formulation equivalent to MPC and the same controller model for learning. The new RL-MPC algorithm can meet constraints and provide similar performance to MPC while enabling continuous learning and the possibility to deal with uncertain environments.

Keywords: Model predictive control; Reinforcement learning; Reinforced model predictive control; Building automation; BOPTEST (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921015932
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:309:y:2022:i:c:s0306261921015932

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.2021.118346

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 ().