Deep reinforcement learning-based residential building energy management incorporating power-to-heat technology for building electrification

Hyung Joon Kim, Jae Yong Lee, Hyunwoo Tak and Dongwoo Kim

Energy, 2025, vol. 317, issue C

Abstract: Building electrification considerably contributes to reducing global energy consumption and carbon emissions, necessitating efficient energy management systems. Although heat pumps (HPs) are promising for power-to-heat (P2H) applications, utilizing single HP systems could limit economic performance and flexibility, particularly in multi-family buildings. Additionally, overestimation and underestimation biases in existing deep reinforcement learning (DRL) algorithms pose substantial challenges for developing optimal energy management strategies under various uncertainties. This study proposes a novel DRL-based residential building energy management system (RBEMS) integrating P2H technology for comprehensive management of electricity, heat, and carbon emissions. First, a two-stage HP system is employed, where a central-HP preheats water to an intermediate temperature, followed by distributed-HP that increases it to the target level, thereby improving system efficiency and enabling flexible control. Second, a novel dynamic action subset–twin delayed deep deterministic policy gradient (TD3) algorithm is proposed to efficiently bridge the gap between action selection and evaluation by selecting high-value subset actions near the optimum from one critic network and evaluating them with a secondary network. Case studies demonstrate that the proposed RBEMS increases total rewards by 20.1 % over a single HP system and 9 % over TD3, achieving lower energy costs, reduced carbon emissions, while ensuring user comforts.

Keywords: Deep reinforcement learning; Electrification; Heat pump; Power-to-heat; Uncertainty; Building energy management system (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225002439
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:energy:v:317:y:2025:i:c:s0360544225002439

DOI: 10.1016/j.energy.2025.134601

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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