Limited data-oriented building heating load prediction method: A novel meta learning-based framework.

Data-driven models have been widely used in building heating load prediction, but often fail when facing limited data. Previous studies have shown transfer learning can assist model learning of target building under limited data by means of other source building data, however, which is subject to the similarity between source and target building. Selecting similar source building data is not easy, especially when the target building is with limited data. This paper, therefore, proposes a novel meta learning-based framework for building heating load prediction. Using meta learning method, a set of promising model parameters is trained by local and global learning on multiple source buildings data. The obtained model parameters has the ability to get quickly trained with few data in each source building, which is further used as model initialization parameters of target building to assist model learning. Framework validity is confirmed by 550 groups of practical buildings data (50 are as target buildings for testing and 500 are as source buildings). The results showed the proposed framework could reduce the prediction errors by 2.04 %∼61.59 % compared with six common transfer learning methods. The novel meta learning-based framework provides an effective solution for building heating load prediction with limited data. [ABSTRACT FROM AUTHOR]