The transfer learning method has gained increasing attention in the domain of building load prediction, particularly in scenarios with limited data samples. Its core principle involves leveraging knowledge obtained from abundant data in source buildings to aid the learning process of models for the target buildings. Existing research has predominantly concentrated on optimizing the selection of source building data to improve transfer learning effectiveness, while the optimization of transfer learning hyper-parameter settings is often neglected. This study proposes a recommendation model tailored for transfer learning hyper-parameter settings in the context of small sample prediction for building heat loads. The objective is to automatically suggest suitable transfer learning hyper-parameter combination based on the specific features of the building heat load data samples. In this study, 200 real building profiles were utilized to generate the input–output dataset required for the recommendation model. By employing data mining techniques such as clustering and classification, the correlation between the features of source building data and the most effective transfer learning hyper-parameter combination is investigated. The developed recommendation model for optimal transfer learning hyper-parameter settings achieves a classification accuracy of 90.5%，and the performance evaluation was conducted using an additional dataset of 30 source buildings. The results show that by employing this recommendation model, the prediction error of the target buildings can be reduced by 0.12% to 6.64% compared to the conventional method of empirically determining transfer learning hyper-parameter settings.

中文翻译：

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一种用于优化迁移学习超参数设置的推荐模型，用于使用有限数据样本构建热负荷预测


迁移学习方法在建筑负载预测领域越来越受到关注，特别是在数据样本有限的场景中。其核心原则涉及利用从源建筑物中的大量数据中获得的知识来帮助目标建筑物的模型学习过程。现有的研究主要集中在优化源构建数据的选择以提高迁移学习的有效性，而迁移学习超参数设置的优化往往被忽视。本研究提出了一种在建筑热负荷小样本预测背景下为迁移学习超参数设置量身定制的推荐模型。目标是根据建筑热负荷数据样本的具体特征自动建议合适的迁移学习超参数组合。在这项研究中，利用 200 个真实的建筑剖面来生成推荐模型所需的输入-输出数据集。通过采用聚类和分类等数据挖掘技术，研究了源构建数据的特征与最有效的迁移学习超参数组合之间的相关性。开发的用于最佳迁移学习超参数设置的推荐模型实现了 90.5% 的分类准确率，并使用 30 个源建筑物的附加数据集进行了性能评估。结果表明，采用这种推荐模型，与经验确定迁移学习超参数设置的传统方法相比，目标建筑物的预测误差可以减少 0.12% 至 6.64%。