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| 基于XGBoost算法的夏热冬冷地区办公建筑围护结构的负荷预测 |
| Load prediction of envelope structure of office buildings in hot summer and cold winter regions based on XGBoost algorithm |
| 投稿时间：2023-11-10 |
| DOI：10.13259/j.cnki.eri.2025.02.004 |
| 中文关键词: 负荷预测 机器学习 EnergyPlus软件 XGBoost算法 |
| 英文关键词:load prediction machine learning EnergyPlus software XGBoost algorithm |
| 基金项目: |
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| 摘要点击次数: 1391 |
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| 中文摘要: |
| 空调负荷预测有助于建筑能源侧的管理与优化，在节能方面具有可观的潜力。为了使预测模型可以快速预测夏热冬冷地区不同建筑的逐时冷负荷，将建筑冷负荷解耦，仅考虑围护结构的负荷预测。首先，建立基于XGBoost算法的基准建筑围护结构逐时冷负荷预测模型，对4种不同特征组合的预测结果进行分析比较，结果表明特征组合D的预测效果最优；然后，基于基准建筑对其他类型建筑的围护结构负荷进行差值修正，得到适用于更多办公建筑的通用预测模型。以上海与杭州的测试建筑为例，利用XGBoost算法预测得到的围护结构逐时冷负荷与利用EnergyPlus软件得到的模拟结果吻合，说明该预测模型具有良好的泛化性，能够精确、有效地预测不同建筑围护结构的冷负荷。 |
| 英文摘要: |
| Accurate air conditioning load prediction enhances building energy management and optimization, demonstrating significant potential for energy savings. To enable rapid hourly cooling load prediction for diverse buildings in hot summer and cold winter zones, this study decouples building cooling loads, focusing specifically on envelope load prediction. First, a baseline XGBoost-based model for hourly envelope cooling load prediction was developed, with comparative analysis of four feature combinations revealing feature set D as optimal. Subsequently, a generalized prediction model adaptable to various office buildings was created by applying differential corrections to the baseline model. Validated against EnergyPlus simulations using test buildings in Shanghai and Hangzhou, the XGBoost-predicted hourly envelope cooling loads showed strong agreement, confirming the model's generalizability and accuracy in predicting envelope thermal performance across different buildings. |
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