Attentive Knowledge Transfer for Short-term Load Forecasting

The modern power system is transitioning towards increasing penetration of renewable energy generation and demand from different types of electrical appliances. With this transition, residential load forecasting, especially short-term load forecasting (STLF), is becoming more and more challenging and important. Accurate short-term load forecasting can help improve energy dispatching efficiency and, as a consequence, reduce overall power system operation cost. Most current load forecasting algorithms assume that there is a large amount of training data available upon which to learn a reliable load forecasting model. However, this assumption can be challenging for real-world applications. In this work, we first propose the use of transfer learning and an attention mechanism to improve short-term load forecasting for a target domain with only a limited amount of available data. Furthermore, we extend the proposed method to utilize heterogeneous features which enables the approach to deal with more complex scenarios in the real world. Experimental results using real-world data sets show that the proposed methods can improve forecasting accuracy by a large margin over several existing baselines.