基于XGBoost算法的夏熱冬冷地區(qū)辦公建筑圍護結(jié)構(gòu)的負荷預測

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中圖分類號：TU832 文獻標志碼：A

Abstract： 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 offour feature combinations revealing feature set D as optimal. Subsequently， a generalized prediction model adaptable to various oice 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.

Keywords： load prediction; machine learning; EnergyPlus software; XGBoost algorithm

隨著人們對舒適生活的不斷追求，社會對于能源的需求與日俱增，其中建筑能耗占社會總能耗的比重也持續(xù)遞增。（剩余7613字）