对能源浪费及其严重环境影响的担忧，对能源消耗的研究和研究具有更大的意义。由于建筑占任何国家总能耗的20%至40%，因此迫切需要具有更多节能特性的节能建筑设计。节能建筑设计的成功主要取决于对维持舒适室内空气条件所需的热负荷（HL）和冷负荷（CL）的准确预测。本文提出了一种数据挖掘方法，用于预测住宅建筑的HL和CL。所提出的技术使用了一种称为bagging的集成方法，该方法将多棵树的预测聚合为基本分类器，而不是依赖于单个预测模型。 该技术使用信息增益和减少错误修剪来构建紧凑的决策树。培训数据包括使用UCI机器学习库（Bache and Lichman 2013）中的Ecotect设计的768栋不同住宅楼的详细信息。该预测模型的相关系数为0.9985，平均绝对误差MAE为0.3811。提出的CL预测模型的相关系数为0.983，平均绝对误差MAE为1.1065。并将该集成方法的性能与作为基本分类器的神经网络进行了比较。引文:美国农业科学院学报-第121卷第2部分，佐治亚州亚特兰大

Concerns about waste of energy and its serious environmental impact have imposed a greater significance for studies and research in energy consumption. Since buildings contribute to 20% to 40% of the overall energy consumption in any country, there is a dire need for energy efficient building designs withmore energy conservation properties.The success of designing an energy efficient building mainly depends on an accurate prediction of heating load (HL) and cooling load (CL) needed to maintain comfortable indoor air conditions. This paper proposes a data mining approach for predictingHL and CL for residential buildings. The proposed technique uses an ensemble method called bagging, which aggregates the predictions made by multiple REPTrees as base classifiers, instead of relying on a single prediction model. The technique uses information gain and reduced error pruning to build compact decision trees. The training data comprises details of 768 diverse residential buildings designed using Ecotect from the UCI machine learning repository (Bache and Lichman 2013).The proposed prediction model forHLhas a correlation coefficient of 0.9985 and a mean absolute error MAE of 0.3811. The proposed prediction model for CL has a correlation coefficient of 0.983 and a mean absolute error MAE of 1.1065. The performance of the proposed ensemble method is also compared with neural networks as a base classifier.