참고문헌
- A. Tokgoz and G. Unal, "A RNN based time series approach for forecasting turkish electricity load," in Proc. of the 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018. DOI: 10.1109/SIU.2018.8404313
- Y. I, Son and S. K. Han, "Demand power forecasting with data mining method," The 47th KIEE Summer Conference, Pyongchang, pp.208-209, 2016. DOI: 10.1109/ISGT-Asia.2017.8378423
- H. S. Tak, T. Y. Kim, H, K, Cho, and H. J. Kim, "A new prediction model for power consumption with local weather information," Journal of The Korea Contents Association, vol.16, no.11, pp. 488-498, 2016. DOI: 10.5392/JKCA.2016.16.11.488
- K. H. Kim, R. J. Park, S. W. Cho, and K. B. Song, "24-Hour load forecasting algorithm using artificial neural network in summer weekdays," Journal of The Korean Institute of Illuminating and Electrical Installation Engineers, vol.31, no.9, pp.103-119, 2017. DOI: 10.5207/JIEIE.2017.31.12.113
- C. H. Park, M. S. Cho, J. U. Park, H. C. Noh, J. U. Lee, and S. H. Park, "Electric load forecasting based on short-and long-term modeling of time series data," in Proc. of the KCC 2019, pp.409-411, 2019.
- S. W. Cho, B. S. Kwon, and K. B. Song, "Day ahead 24-hours load forecasting algorithm using latest weather forecasting," The Transactions of The Korean Institute of Electrical Engineers, vol.68, no.3, pp.416-422, 2019. DOI: 10.5370/KIEE.2019.68.3.416
- D. H. Kang, J. D. Park, and K. B. Song, "24-hour load forecasting for anomalous weather days using hourly temperature," The Transactions of The Korean Institute of Electrical Engineers, vol.65, no.7, pp.1144-1150, 2016. DOI: 10.5370/KIEE.2016.65.7.1144
- M. Q. Raza and A, Khosravi, "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," ELSEVIER Renewable and Sustainable Energy Reviews, vol.50, pp.1352-1372, 2015. DOI: 10.1016/j.rser.2015.04.065
- F. J. Ordonez and D. Roggen, "Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition," Sensors, pp.3-9, 2016. DOI: 10.3390/s16010115
- C. Olah, "Understanding LSTM networks," https://colah.github.io/posts/2015-08-Understanding-LSTMs.
- H. C. Chung et al., "Prediction for energy demand using 1D-CNN and bidirectional LSTM in Internet of energy," Journal of Institute of Korean Electrical and Electronics Engineers, vol.23, no.1, pp.134-142, 2019. DOI: 10.7471/ikeee.2019.23.1.134
- Google, "Tensorflow Tutorial," https://www.tensorflow.org/tutorials.
- H. K. Choi, B. H. Chang, and K. H. Kim, "Comparative study of short-term load forecasting with deep learning algorithm," The 50th KIEE Summer Conference, pp.664-665, 2019. DOI: 10.1109/GTSD.2018.8595514
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- 가전제품 전력 사용 분류를 위한 장단기 메모리 기반 비침입 부하 모니터링 기법 vol.21, pp.4, 2019, https://doi.org/10.7236/jiibc.2021.21.4.109