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Energies 2019, 12(8), 1433; https://doi.org/10.3390/en12081433

Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China
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Received: 22 January 2019 / Revised: 30 March 2019 / Accepted: 9 April 2019 / Published: 14 April 2019
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min. View Full-Text
Keywords: short-term load forecasting; back-propagation neural network; recurrent neural network; long-short term memory; gate-recurrent neural network short-term load forecasting; back-propagation neural network; recurrent neural network; long-short term memory; gate-recurrent neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, L.; Yang, H. Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting. Energies 2019, 12, 1433.

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