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Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems
Energy and Buildings ( IF 6.6 ) Pub Date : 2024-12-12 , DOI: 10.1016/j.enbuild.2024.115162 Jie Lu, Chaobo Zhang, Bozheng Li, Yang Zhao, Ruchi Choudhary, Max Langtry
Energy and Buildings ( IF 6.6 ) Pub Date : 2024-12-12 , DOI: 10.1016/j.enbuild.2024.115162 Jie Lu, Chaobo Zhang, Bozheng Li, Yang Zhao, Ruchi Choudhary, Max Langtry
Digital twin models serve as the most crucial foundation for estimating the expected conservation effects of building energy systems. The establishment of digital twin models relies heavily on the detailed design parameters of building energy systems, including component topology, power, and efficiency. However, a considerable number of existing buildings lack complete modeling parameters. It is challenging to impute the incomplete parameters for building energy systems, since the different building energy systems have different modeling parameters and different amounts of incomplete parameters. To address this challenge, a self-attention variational autoencoder-based method is proposed. Its basic idea is to employ the self-attention mechanism to identify the key parameters within the entire parameters, and to learn the relationships between the incomplete parameters and the key parameters. The method comprises three steps: feature embedding, model building, and supervised learning. The feature embedding is employed to represent the features of building energy systems, with each feature carrying a physical meaning. A self-attention variational autoencoder model is then applied to impute the incomplete parameters based on the available parameters and the relationship between the incomplete parameters and the key parameters. Supervised learning is adopted to train the model based on the principles of the evidence lower bound. The evaluations are conducted using 350 various practical building energy systems sourced from the excellent design atlas in China. Three representative incomplete parameter imputation methods are selected as baseline models for performance comparison. The results indicate that the proposed method exhibits high imputation accuracy, good flexibility, and good interpretability.
更新日期：2024-12-12
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