Abstract
Load forecasting is an asset for sustainable building energy management, as accurate predictions enable efficient energy consumption and con- tribute to decarbonisation efforts. However, data-driven models are often limited by dataset length and quality. This study investigates the effectiveness of transfer learning (TL) for load forecasting in office buildings, with the aim of addressing data scarcity issues and improving forecasting accuracy. The case study consists in a group of eight virtual buildings (VB) located in Porto, Portugal. VB A2 serves as pre-trained base model to transfer knowledge to the remaining VBs, which are analysed in varying degrees of data availability. Our findings indicate that TL can significantly reduce training time, for up to 87%, while maintaining accuracy levels comparable to those of models trained with full dataset, and exhibiting superior performance when com- pared to models trained with scarce data, with average RMSE reduction of 42.76%.