A Deep Learning Based Real-time Load Forecasting Method in Electricity Spot Market

被引：5
|
作者：
Zhang, Qipei [1 ]
Lu, Jixiang [1 ]
Yang, Zhihong [1 ,2 ]
Tu, Mengfu [1 ]
机构：
[1] Nari Technol Co Ltd, Nanjing 211106, Jiangsu, Peoples R China
[2] State Key Lab Intelligent Power Grid Protect & Op, Nanjing 211106, Jiangsu, Peoples R China
来源：
2018 INTERNATIONAL SEMINAR ON COMPUTER SCIENCE AND ENGINEERING TECHNOLOGY (SCSET 2018) | 2019年 / 1176卷
关键词：
D O I：
10.1088/1742-6596/1176/6/062068
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper analyzes the potential influence in Chinese electricity market due to the reform and access of the electricity spot market. On this occasion, a deep learning based model for load forecasting is proposed to improve the market operator's precise scheduling level and assist power retailers in managing bid strategies. Long-Short Term Memory (LSTM) unit is used to modeling, which is one of the most popular techniques of deep learning. In addition, historical power load data and meteorological data of Suzhou and Lianyungang in China from January 2015 to December 2017 are used for the study to training and evaluate forecasting model. As a result, this paper shows the compare results with exiting machine algorithm for load forecasting.
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页数：9
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