Enhancing source domain availability through data and feature transfer learning for building power load forecasting

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.