基于改进XGBoost超参数优化的地下工程空调系统负荷预测

针对地下工程空调负荷难以精确预测的问题，提出一种基于天牛须搜索算法（Beetle Antennae Search， BAS）优化极限梯度提升算法（eXtreme Gradient Boosting， XGBoost）的负荷预测模型。该算法通过引入典型最优解引导机制优化常规BAS算法中的位置更新策略，同时采用线性递减策略对天牛的搜索步长进行修正，以实现更快达到全局最优点，提高收敛速度；并利用改进的BAS算法对XGBoost中的决策树个数、树的最大深度2个对模型预测精度有较大影响的超参数进行寻优，以获得XGBoost的最优参数组合，提高模型预测精度。最后，以某地下保障工程空调系统为研究对象，验证所提出的预测模型的有效性。

Energy Consumption Prediction of Air-conditioning System in Underground Engineering Based on XGBoost Hyperparameter Optimization

Aiming at the problem that it is difficult to accurately predict the air-conditioning system’s energy consumption in underground engineering， an energy consumption prediction model based on the eXtreme Gradient Boosting algorithm （XGBoost） optimized by the Beetle Antennae Search （BAS） algorithm is proposed. The algorithm optimizes the position update strategy in the conventional beetle algorithm by introducing a typical optimal solution guidance mechanism， and uses a linear decreasing strategy to correct the search step size of the beetle， so as to achieve the global optimum point and improve the convergence speed. The number of decision trees and the maximum depth of the tree in XGBoost， which have a greater impact on the prediction accuracy of the mode， are used to optimize by the improved BAS， so as to obtain the optimal parameter combination of XGBoost and improve the model prediction accuracy. Finally， taking the air-conditioning system of an underground security project as the research object， the validity of the proposed prediction model is verified.