Morning Poster Session: Electric Load Forecasting with Boosting based Sample Transfer

With the increasing adoption of renewable energy generation and different types of electric devices, electric load forecasting, especially short-term load forecasting (STLF), is attracting more and more attention. Accurate short-term load forecasting is of significant importance for the safety and efficiency of power grids. Deep learning based models have shown impressive success on several applications including short-term load forecasting. However, for several real-world scenarios, it may be very difficult or even impossible to collect enough training data to learn a reliable machine learning model. Specifically, we first proposed In this paper, we propose an instance transfer-based transfer learning algorithm to assist the learning performance for the short-term load forecasting. The proposed algorithm is evaluated on several real-world data sets and has shown significant improvements over the baselines.