• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

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P. H. Kuo, C. J. Huang, Nov 2018, A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting, Energies, 213DOI
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C. Tian, J. Ma, C. Zhang, P. Zhan, Nov 2018, A Deep Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolution Neural Network, Energies, 3493DOI
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J. Runhai, Z. Tianming, J. Yizhi, H. Hui, Oct 2018, Short-Term Non-Residential Load Forecasting Based on Multiple Sequences LSTM Recurrent Neural Network, IEEE Access, Vol. 6, pp. 50438-59448DOI
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K. Weicong, D. Z. Yang, J. Youwei, J. H. David, X. Yan, Z. Yuan, Jan 2019, Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network, IEEE Trans. on Smart Grid, Vol. 10, No. 1, pp. 841-850DOI
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B. Kwon, R. Park, K. Song, Dec 2019, Weekly Peak Load Forecasting for 104 Weeks Using Deep Learning Algorithm, in Proceedings of APPEEC 2019, Vol. macau, No. ChinaDOI
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Lim, S. Kim, J. Park, K. Song, Feb 2013, Representative Temperature Assessment for Improvement of Short-Term Load Forecasting Accuracy, Journal of KIIEIE, Vol. 27, No. 6, pp. 39-43DOI