Active Deep Multi-task Learning for Forecasting Short-Term Loads

Abstract

With the increasing adoption of renewable energy generation and electric devices, electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important. The widespread adoption of smart meters makes it possible to utilize complex machine learning models for both aggregated load and single-home residential load forecasting. Similar homes in nearby locations are likely to have similar load consumption patterns and this similarity can be used to improve the overall forecasting performance. However, most current work on load forecasting focuses on single learning task without exploiting the benefit of joint learning. In this paper, we propose the use of the multi-task learning (MTL) framework with long short-term memory (LSTM) recurrent neural networks for both aggregated and single home STLF. We propose a MTL-based forecasting algorithm for aggregated load forecasting in which single home forecasting is formulated as a single learning task within the MTL framework. This algorithm is extended for single home load forecasting in which load forecasting for a particular home becomes the primary learning task. Experimental results on real-world data sets demonstrate that residential load forecasting for both aggregated load and a single home can be improved within the MTL framework.