Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning

Yanxue Li, Zixuan Wang, Wenya Xu, Weijun Gao, Yang Xu and Fu Xiao

Energy, 2023, vol. 277, issue C

Abstract: Efficient and flexible energy management strategy can play an important role in energy conservation in building sector. The model-free reinforcement learning control of building energy systems generally requires an enormous amount of training data and low learning efficiency creates an obstacle to practice. This work proposes a hybrid model-based reinforcement learning framework to optimize the indoor thermal comfort and energy cost-saving performances of a ZEH (zero energy house) space heating system using relatively short-period monitored data. The reward function is designed regarding energy cost, PV self-consumption and thermal discomfort, proposed agents can interact with the reduced-order thermodynamic model and an uncertain environment, and makes optimal control policies through the learning process. Simulation results demonstrate that proposed agents achieve efficient convergence, D3QN presents a superiority of convergence performance. To evaluate the performances of proposed algorithms, the trained agents are tested using monitored data. With learned policies, the self-learning agents could balance the needs of thermal comfort, energy cost saving and increasing on-site PV consumption compared with the baselines. The comparative analysis shows that D3QN achieved over 30% cost savings compared with measurement results. D3QN outperforms DQN and Double DQN agents in test scenarios maintaining more stable temperatures under various outside conditions.

Keywords: ZEH; Thermal comfort; Deep reinforcement learning; Energy management strategy (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010216

DOI: 10.1016/j.energy.2023.127627

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