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Short-term carbon emission prediction method of green building based on IPAT model

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  • Peng Fang

Abstract

In order to solve the problems of high complexity and low prediction accuracy of green building carbon emission prediction process, this paper proposes a green building short-term carbon emission prediction method based on IPAT model. The IPCC method is used to determine the influencing factors of carbon emission of green buildings, and the classification of influencing factors is completed according to the importance of influencing factors. The IPAT model is established to decompose the carbon emission into the products of different factors, and the model is used to predict the short-term carbon emission of green building construction stage and the whole stage. The experimental results show that the prediction time of this method is always less than 4 s and the prediction accuracy is always higher than 95%, which proves that this method has fast prediction process and high accuracy, and realises the design expectation.

Suggested Citation

  • Peng Fang, 2023. "Short-term carbon emission prediction method of green building based on IPAT model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 45(1), pages 1-13.
    • Handle: RePEc:ids:ijgeni:v:45:y:2023:i:1:p:1-13
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    Citations

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    Cited by:

    1. Jing Liang & Lingying Pan, 2023. "Effect of Scale and Structure Changes of China’s High-Carbon Industries on Regional Carbon Emissions," Energies, MDPI, vol. 16(18), pages 1-17, September.
    2. Wei Yang & Qiheng Yuan & Yongli Wang & Fei Zheng & Xin Shi & Yi Li, 2023. "Carbon Emission Forecasting Study Based on Influence Factor Mining and Mini-Batch Stochastic Gradient Optimization," Energies, MDPI, vol. 17(1), pages 1-18, December.
    3. Yong Xiao & Cheng Yong & Wei Hu & Hanyun Wang, 2023. "Factors Influencing Carbon Emissions in High Carbon Industries in the Zhejiang Province and Decoupling Effect Analysis," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
    4. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.

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