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  title = {Utilizing interpretable stacking ensemble learning and NSGA-III for the prediction and optimisation of building photo-thermal environment and energy consumption},
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@article{Zhu2024,
  title = {TSILNet: A novel hybrid model for energy disaggregation based on two-stage improved TCN combined with IECA-LSTM},
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@article{Zhang2025,
  title = {An unsupervised method of HVAC energy disaggregation and demand response potential estimation},
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@article{Yu2025,
  title = {Prediction of campus office occupancy and AC behavior based on Transformer and the collective impact on energy consumption},
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@article{Li2025,
  title = {Impact of seasonal wind and building morphology on microclimate and building energy consumption},
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@article{Tan2025,
  title = {A collaborative mixture of experts framework for building energy consumption prediction},
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@article{Yang2025,
  title = {Hybrid physics-neural-network MPC with stochastic disturbance forecasting for high-inertia thermally activated building systems},
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@article{Zeng2025,
  title = {AI-based generation and optimization of energy-efficient residential layouts controlled by contour and room number},
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@article{Garboushian2025,
  title = {Adaptive reuse strategies in urban centers: Experimental validation using neural networks and real building data},
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@article{Fang2025,
  title = {Embedding physical neurons in physics-informed neural networks (EP-PINNs) for enhancing chiller performance prediction},
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@article{Ran2025,
  title = {Performance evaluation of physical consistency approach for optimizing residential space heating system in cold snap conditions},
  author = {Ran, Yi and Wang, Zixuan and Li, Yanxue and Xiao, Fu},
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@article{Ma2025,
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@article{Fahim2025,
  title = {ResiDualNet: A novel electric vehicle charging data imputation technique to enhance load forecasting accuracy},
  author = {Fahim, Belal Mahmud and Akbar, Mohammad Kaosain and Amayri, Manar},
  journal = {Building Simulation},
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@article{Zhao2025,
  title = {Day-ahead energy management of a smart building energy system aggregated with electrical vehicles based on distributionally robust optimization},
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  journal = {Building Simulation},
  year = {2025},
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@article{Lu2025,
  title = {Graph convolutional networks-based method for uncertainty quantification of building design loads},
  author = {Lu, Jie and Zheng, Zeyu and Zhang, Chaobo and Zhao, Yang and Feng, Chenxin and Choudhary, Ruchi},
  journal = {Building Simulation},
  year = {2025},
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  url = {https://doi.org/10.1007/s12273-024-1212-8}
}