As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.

中文翻译：

---

基于随机森林和BP神经网络的住宅建筑能耗联合预测模型研究

住宅建筑能耗在建筑能耗中占很大比重，提高住宅建筑能效对绿色发展具有重要意义。为了更有效地评估住宅建筑能耗，本文提出了一种基于随机森林和BP神经网络的组合预测模型（RF-BPNN）。为验证RF-BPNN组合模型的预测效果，利用UCI数据库中的能效数据集进行实验，用5个指标对模型进行评价：平均绝对误差、均方根偏差、平均绝对百分比误差、相关系数和重合指数。分别与随机森林、BP神经网络模型和其他现有模型相比，