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- W4310971472 abstract "Indoor temperature modeling has been a vital component to develop accurate digital twins and smart controllers for buildings. Hybrid (also known as gray-box) modeling caught significant attention from the literature for this task. Combining the accumulated physical knowledge we have about thermal behavior with modern data-driven techniques promises more accurate and stable prediction models which can be used in various applications. However, methods such as data-driven parameter optimization and constrained training proposed in the literature show practical limitations such as high computational expense and software incompatibilities. In this paper we propose a transfer learning-based hybrid modeling approach where a CNN-LSTM model is pre-trained with the simulation data and then refined with the real-life data, thus, creating a completely data-driven hybrid model. We compared our approach to the same CNN-LSTM architecture trained only on real-life data. We reported significant accuracy and stability increases with the proposed approach." @default.
- W4310971472 created "2022-12-21" @default.
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- W4310971472 date "2022-10-17" @default.
- W4310971472 modified "2023-10-18" @default.
- W4310971472 title "Transfer Learning-based Hybrid Modeling Approach for Indoor Temperature Modeling" @default.
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- W4310971472 doi "https://doi.org/10.1109/iecon49645.2022.9968939" @default.
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