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Title	Power Load Prediction Based On An Improved Clock-Work Rnn
ID_Doc	42542
Authors	Huang F.; Zhuang S.; Yu Z.
Year	2019
Published	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI	http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00140
Abstract	Through accurate power load prediction, the smart grid can provide more efficient, reliable and environmentally friendly power services than the traditional power grid. In recent years, Recurrent Neural Networks (RNNs) have received more and more attention in power load prediction because traditional machine learning models cannot capture the time dependencies that often exist in power load data. However, due to the self-connection of the hidden layer, the vanishing gradient problem is very easy to occur with the increasing depth of the simple RNN, which leads to the decline of the prediction accuracy. In order to solve this problem, this paper adopts the recently developed RNN architecture called Clock-Work RNN (CW-RNN) on the task of one day ahead prediction. The research focuses on the construction strategy of recurrent connection matrix related to the hidden layer in CW-RNN. An improved CW-RNN named CW-RNN-SCR adopts the strategy of connecting adjacent modules into a simply closed ring, which not only improves the prediction accuracy but also reduces the number of parameters required for training. Experimental results show that the improved CW-RNN can achieve better performance than the traditional RNN architectures. © 2019 IEEE.
Author Keywords	Cw-rnn; Gru; Lstm; Power load prediction; Srnn

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