A Deep Learning Based Framework for Power Demand Forecasting with Deep Belief Networks

Abstract

Power demand forecasting plays a very important role in many electricity-required industries, such as modern high-speed railways or urban railways. Accurate forecasting will guarantee that electrical equipments such as electric traction systems for trains work under safe, robust and efficient status. Recently, many studies adopt the learning-based methods to achieve the prediction of power demand. However, most of the studies use the traditional classification or clustering algorithms which may not satisfy the requirements of accuracy and efficiency due to the complex features in smart grid. In this paper, we focus on solving the power demand forecasting problem based on deep learning structures. We first propose a deep learning based framework for power demand forecasting with Deep Belief Network (DBN). Then, we use an algorithm called Adaboost to combine weak learners with strong learners, which can increase the accuracy significantly in real-world scenarios. The prediction of the load status is realized by analyzing the information of historical distribution transformer load, weather, electricity population and some other related information. It is also worth noting that the training process of these DBN networks can be parallel, which effectively shorten the processing time and provide the possible of real-time predicting. Our experiment on real-world data from the electrical company shows results that the deep leaning based methods can increase the accuracy of forecasting and significantly shorten the prediction time.