A hybrid transfer learning model for short-term electric load forecasting

Abstract

Transfer learning approach can be applied to electric load forecasting because electric load data from nearby locations are significantly correlated. However, ordinary transfer learning methods may bring negative transfer into load forecasting as time series prediction is not exactly the same as traditional data regression problem. Consequently, this paper proposes a novel hybrid transfer learning model based on time series decomposition. Firstly, trend and seasonal components are handled by standard machine learning approach so that seasonal cycles of electric load data can be interpreted better. Secondly, two-stage transfer regression model is established to forecast the irregular component in order to improve the forecasting accuracy. The negative transfer is successfully avoided, and the prediction accuracies are significantly improved because of time series decomposing and the additional information provided by the related dataset. The case study presented by two real-world power load datasets illustrates that the proposed approach can improve electric load prediction for a location by 30% at most by using additional data from another location.