Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings

Zhou, Congyue; Wang, Zhu; Wang, Xuanli; Guo, Rui; Zhang, Zhao; Xiang, Xingwei; Wu, Yiqun

doi:10.1016/j.buildenv.2024.112035

Building and Environment

2024

DOI: 10.1016/j.buildenv.2024.112035

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Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings

Congyue Zhou,

Zhu Wang,

Xuanli Wang

et al.

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2024

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Cited by 4 publications

References 61 publications

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Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-informed

Li,

Zhan,

Fang

et al. 2024

Energy

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Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-informed

Li,

Zhan,

Fang

et al. 2024

Energy

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Prediction of formation energy for oxides in ODS steels by machine learning

Yang,

Dou

2024

Materials & Design

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Edge Computing in Power IoT Security and Intelligent Monitoring

Liu,

Wang,

Chen

et al. 2024

Applied Mathematics and Nonlinear Sciences

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In the context of the rapid development of power IoT, the application of edge computing technology has become the key to improving the level of grid intelligence and enhancing the data processing capability. This paper initially designs the edge computing system for electric power IoT based on the edge computing model. Key-edge computing technologies are combined to process and analyze power IoT data in real-time. Simulation experiments have formed and verified an intelligent security monitoring system for electric power IoT using the LightGBM algorithm. The training convergence speed and effectiveness of this paper’s scheme are better than Stroj’s scheme, and this paper’s scheme can increase the security of power IoT data through key generation and filter de-duplication. This paper’s nodes have an average synchronization time of 9.25 ms. The 128MB data node has an upload time of 57143ms. The data sharing time is about 292~7489 ms faster than the comparison scheme, and in the data search phase, the time overhead of this paper’s scheme is less than the comparison scheme. In summary, this paper’s constructed security monitoring system can offer robust technical support for the advancement of intelligent, efficient, and omnipotent power in the Internet of Things.

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Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings

Cited by 4 publications

References 61 publications

Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-informed

Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-informed

Prediction of formation energy for oxides in ODS steels by machine learning

Edge Computing in Power IoT Security and Intelligent Monitoring

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