Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system

Chen, Siliang; Ge, Wei; Liang, Xinbin; Jin, Xinqiao; Du, Zhimin

doi:10.1016/j.apenergy.2023.122189

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…For instance, the recent “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) database reported more than 12,000 molecule candidates that could be used for AORFBs . To address this, continual learning (CL) has emerged as a promising approach, allowing models to update and expand using solely new data, without forgetting previous acquired knowledge. , Although CL has gained attention in energy storage, − its application in flow batteries, particularly AORFBs, has not been reported yet.…”

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confidence: 99%

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Fu,

Howard,

Zeng

et al. 2024

ACS Energy Lett.

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Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness, and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using an AORFB database with a thousand potential materials generated by a 780 cm 2 interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training strategies to retain previously learned battery material knowledge. The trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers.

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