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By the end of 2020, carbon emissions from the building process accounted for over 51% of China’s total emissions. Achieving carbon neutrality is a major challenge for the construction industry and a key step in reaching dual carbon goals. In urban planning, considering both solar PV potential (source) and energy consumption (saving) of buildings can provide a scientific and comprehensive energy demand assessment. However, current methods for predicting PV potential and energy consumption face challenges in efficiency and data availability. This study aims to use multi-source data and generative algorithms to predict the net energy consumption levels of urban blocks.

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Prediction of Net Energy Consumption Potential of District Buildings Based on Generative Adversarial Network Models and Multi-source Image Data

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  • 发表日期：

    2025年

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  • Jiawei Yao

摘要

By the end of 2020, carbon emissions from the building process accounted for over 51% of China’s total emissions. Achieving carbon neutrality is a major challenge for the construction industry and a key step in reaching dual carbon goals. In urban planning, considering both solar PV potential (source) and energy consumption (saving) of buildings can provide a scientific and comprehensive energy demand assessment. However, current methods for predicting PV potential and energy consumption face challenges in efficiency and data availability. This study aims to use multi-source data and generative algorithms to predict the net energy consumption levels of urban blocks.