基于样本集质量的建筑能耗预测机器 学习算法选择及参数设置

使用机器学习算法对建筑能耗进行预测正逐渐成为建筑设计初期重要的决策辅助工具，机器学习算法的选择及其参数设置一直是机器学习领域研究的热点和难点。但现有研究大多从算法原理角度进行预测模型的选择及参数设置，训练样本集的特征信息未得到充分利用。为此，提出一种以样本量及样本分布特征为出发点的样本集质量分类方法，针对不同质量样本集测试不同机器学习算法的学习性能，制定不同质量样本集的算法选择及参数设置策略。分析样本特征与算法性能之间的关系，为建筑设计提供有效指导。

Selection of building energy consumption prediction machine learning algorithms and parameter setting based on quality of samples

Machine learning algorithms are playing a more important role in building energy consumption prediction during the conceptual design. The selection of the machine learning algorithms and parameter setting have become a focus in the field of building performance design. However, the algorithms and their parameters are usually determined by the principle of algorithms rather than the features of the training samples which also have an effect on the performance of algorithms. Therefore, a classification method based on the quality of training samples which is evaluated by sample size and sample distribution characteristics is proposed. The performance of different machine learning algorithms for different quality sample sets is tested, and algorithm selection and parameter setting strategies for different quality sample sets are formulated. The relationship between sample quality and algorithm performance is investigated to provide effective guidance for architects.