During the initial phases of operation following the construction or renovation of existing buildings, the availability of historical power usage data is limited, which leads to lower accuracy in load forecasting and hinders normal usage. Fortunately, by transferring load data from similar buildings, it is possible to enhance forecasting accuracy. However, indiscriminately expanding all source domain data to the target domain is highly likely to result in negative transfer learning. This study explores the feasibility of utilizing similar buildings (source domains) in transfer learning by implementing and comparing two distinct forms of multi-source transfer learning. Firstly, this study focuses on the Higashita area in Kitakyushu City, Japan, as the research object. Four buildings that exhibit the highest similarity to the target buildings within this area were selected for analysis. Next, the two-stage TrAdaBoost.R2 algorithm is used for multi-source transfer learning, and its transfer effect is analyzed. Finally, the application effects of instance-based (IBMTL) and feature-based (FBMTL) multi-source transfer learning are compared, which explained the effect of the source domain data on the forecasting accuracy in different transfer modes. The results show that combining the two-stage TrAdaBoost.R2 algorithm with multi-source data can reduce the CV-RMSE by 7.23% compared to a single-source domain, and the accuracy improvement is significant. At the same time, multi-source transfer learning, which is based on instance, can better supplement the integrity of the target domain data and has a higher forecasting accuracy. Overall, IBMTL tends to retain effective data associations and FBMTL shows higher forecasting stability. The findings of this study, which include the verification of real-life algorithm application and source domain availability, can serve as a theoretical reference for implementing transfer learning in load forecasting.

在既有建筑新建或改造后的运营初期，历史用电数据的可用性有限，导致负荷预测的准确性较低，影响正常使用。幸运的是，通过传输类似建筑物的负载数据，可以提高预测准确性。然而，不加区别地将所有源域数据扩展到目标域很可能导致负迁移学习。本研究通过实施和比较两种不同形式的多源迁移学习，探讨了在迁移学习中利用相似建筑物（源域）的可行性。首先，本研究以日本北九州市东田地区为研究对象。选择与该区域内的目标建筑物最相似的四栋建筑物进行分析。接下来，采用两阶段TrAdaBoost.R2算法进行多源迁移学习，并分析其迁移效果。最后，比较了基于实例（IBMTL）和基于特征（FBMTL）的多源迁移学习的应用效果，解释了不同迁移模式下源域数据对预测精度的影响。结果表明，两阶段TrAdaBoost.R2算法与多源数据相结合，相比单源域，CV-RMSE降低了7.23%，精度提升显着。同时，基于实例的多源迁移学习可以更好地补充目标领域数据的完整性，具有更高的预测精度。总体而言，IBMTL 倾向于保留有效的数据关联，而 FBMTL 显示出更高的预测稳定性。这项研究的结果包括现实生活中算法应用和源域可用性的验证，可以为在负荷预测中实施迁移学习提供理论参考。