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Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer

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Abstract

Short-term load forecasting (STLF) is essential for power system operation. STLF based on deep neural network using LSTM layer is proposed. In order to apply the forecasting method to STLF, the input features are separated into historical and prediction data. Historical data are input to long short-term memory (LSTM) layer to model the relationships between past observed data. The outputs of the LSTM layer are incorporated with outputs of fully-connected layer in which prediction data, for instance weather information for forecasting day, are input. The optimal parameters of the proposed forecasting method are selected following several experiment. The proposed method is expected to contribute to stable power system operation by providing a precise load forecasting.

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Acknowledgements

This research was supported by Korea Electric Power Corporation (Grant number: R18XA04) and “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20184010201690).

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Authors and Affiliations

  1. Department of Electrical Engineering, Soongsil University, Seoul, South Korea

    Bo-Sung Kwon, Rae-Jun Park & Kyung-Bin Song

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  1. Bo-Sung Kwon
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  2. Rae-Jun Park
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  3. Kyung-Bin Song
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Correspondence to Kyung-Bin Song.

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Kwon, BS., Park, RJ. & Song, KB. Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer. J. Electr. Eng. Technol. 15, 1501–1509 (2020). https://doi.org/10.1007/s42835-020-00424-7

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