Browse > Article
http://dx.doi.org/10.7471/ikeee.2019.23.3.852

Deep Learning Based Short-Term Electric Load Forecasting Models using One-Hot Encoding  

Kim, Kwang Ho (Dept. of Electrical and Electronics Engineering, Kangwon National University)
Chang, Byunghoon (Hankook Electric Power Information Co.)
Choi, Hwang Kyu (Dept. of Computer Science and Engineering, Kangwon National University)
Publication Information
Journal of IKEEE / v.23, no.3, 2019 , pp. 852-857 More about this Journal
Abstract
In order to manage the demand resources of project participants and to provide appropriate strategies in the virtual power plant's power trading platform for consumers or operators who want to participate in the distributed resource collective trading market, it is very important to forecast the next day's demand of individual participants and the overall system's electricity demand. This paper developed a power demand forecasting model for the next day. For the model, we used LSTM algorithm of deep learning technique in consideration of time series characteristics of power demand forecasting data, and new scheme is applied by applying one-hot encoding method to input/output values such as power demand. In the performance evaluation for comparing the general DNN with our LSTM forecasting model, both model showed 4.50 and 1.89 of root mean square error, respectively, and our LSTM model showed high prediction accuracy.
Keywords
Electric Load Forecasting; LSTM; One-Hot Encoding; RNN; Virtual Power Plant;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 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   DOI
2 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   DOI
3 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   DOI
4 Google, "Tensorflow Tutorial," https://www.tensorflow.org/tutorials.
5 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   DOI
6 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   DOI
7 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   DOI
8 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   DOI
9 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.
10 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   DOI
11 C. Olah, "Understanding LSTM networks," https://colah.github.io/posts/2015-08-Understanding-LSTMs.
12 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   DOI
13 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   DOI