An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios

The use of transfer learning for building’s electricity loads prediction has shown great potential when in practice the available electricity consumption data in local energy communities is sparse or of unacceptable quality. However, current model based transfer learning applications largely focus on knowledge transfer from whole data of similar building(s). Domain deviation will probably occur and lead to degraded prediction performance if the selected source domain(s) is difficult to align with the target one in their distributions. To address this issue, this study proposes an instance based multi-source transfer learning framework for short-term electricity loads prediction. A two-stage similarity metric with a boosting style ensemble learning method is provided. Different from commonly used transfer learning, after selecting similar buildings, the most helpful data samples from source domains will be further picked by the Nearest Neighbor Search method, and an improved TrAdaBoost algorithm based on data weighting and reusing is designed for knowledge transfer and prediction. The proposed strategy is applied on two different educational buildings’ hourly energy forecasting with sparse historical data. The influences of source domain numbers and target data quantities on prediction performance are fully investigated. The results show that by using supervised selection of similar buildings’ number, the proposed transfer learning could achieve a 20% error reduction than single-source transfer learning model. Compared with previous multi-source transfer learning model, the proposed strategy also proves its superior prediction accuracy (1.97% vs 2.26%, MAPE).