This paper analyzes the potential influence in Chinese electricitymarket due to the reform and access of the electricityspotmarket. On this occasion, a deeplearningbased model for loadforecasting is proposed to ...
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This paper analyzes the potential influence in Chinese electricitymarket due to the reform and access of the electricityspotmarket. On this occasion, a deeplearningbased model for loadforecasting is proposed to improve the market operator's precise scheduling level and assist power retailers in managing bid strategies. LongShort Term Memory(LSTM) unit is used to modeling, which is one of the most popular techniques of deeplearning. 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 loadforecasting.
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