References
- Ahn, B., Choi, H., Lee, H., Regional long-term/midterm load forecasting using SARIMA in South Korea, Journal of the Korea Academia-Industrial Cooperation Society, 2015, Vol. 16, No. 12, pp. 8576-8584. https://doi.org/10.5762/KAIS.2015.16.12.8576
- Amjady, N., Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Trans. on Power Systems, 2002, Vol. 16, No. 4, pp. 798-805. https://doi.org/10.1109/59.962429
- Box, G., Jenkins, G., and Reinsel, G., Time Series Analysis : Forecasting and Control, 4th Edition, Wiley, Hoboken, New Jersey, 2008.
- Elman, J., Finding Structure in Time, Cognitive Science, 1990, Vol. 14, pp. 179-211. https://doi.org/10.1207/s15516709cog1402_1
- Heideman, M., Johnson, D., and Burrus, C., Gauss and the history of the fast Fourier transform, IEEE ASSP Magazine, 1984, Vol. 1, No. 4, pp. 14-21. https://doi.org/10.1109/MASSP.1984.1162257
- Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural Computation, 1997, Vol. 9, pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Kang et al., BIM-based Data Mining Model for Effective Energy Management, Journal of the Korea Academia-Industrial Cooperation Society, 2015, Vol. 16, pp. 5591-5599. https://doi.org/10.5762/KAIS.2015.16.8.5591
- Lee et al., A study on the estimation and prediction of electricity peak, Korean Energy Economic Review, 2010, Vol. 9, pp. 83-99.
- Lee, C., Song, G,. and Kim, J., A Study on the Prediction of Power Consumption in the Air-Conditioning System by Using the Gaussian Process, Journal of Society of Korea Industrial and Systems Engineering, 2016, Vol. 39, No. 1, pp. 64-72. https://doi.org/10.11627/jkise.2016.39.1.064
- Lee, C., Song, G., and Kim, J., Correlation Analyses of the Temperature Time Series Data from the Heat Box for Energy Modeling in the Automobile Drying Process, Journal of Society of Korea Industrial and Systems Engineering, 2014, Vol. 37, pp. 27-34. https://doi.org/10.11627/jkise.2014.37.2.27
- Lee, H. and Shin, H., Electricity Demand Forecasting based on Support Vector Regression, IE Interfaces, 2011, Vol. 24, pp. 351-361. https://doi.org/10.7232/IEIF.2011.24.4.351
- Nam, B., Song, K., Kim, K., and Cha, J., The spatial electric load forecasting algorithm using the multiple regression analysis method, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 2008, Vol. 22, No. 2, pp. 63-70. https://doi.org/10.5207/JIEIE.2008.22.2.063
- Oh, D., The present and future of big data in the electric industry, Journal of the Electric World, 2014, pp. 18-23.
- Papalexopoulos, A. and Hesterberg, T., A Regression-Based Approach to Short-Term System Load Forecasting, IEEE Trans. on Power Systems, 1990, Vol. 4, No. 4, pp. 1535-1547. https://doi.org/10.1109/59.99410
- Park, Y. and Wang, B., Neuro-fuzzy model based electrical load forecasting system : Hourly, daily, and weekly forecasting, Journal of Korean Institute of Intelligent Systems, 2004, Vol. 14, No. 5, pp. 533-538. https://doi.org/10.5391/JKIIS.2004.14.5.533
- R : Holt-Winters Filtering, stat.ethz.ch. Retrieved 2018-01-05.
- Saini, L. and Soni, M., Artificial neural network-based peak load forecasting using conjugate gradient methods, IEEE Trans. on Power Systems, 2002, Vol. 17, No. 3, pp. 907-912. https://doi.org/10.1109/TPWRS.2002.800992
- Schmidhuber, J., Deep Learning in Neural Networks : An Overview, Neural Networks, 2015, Vol. 61, pp. 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
- Senjyu et al., One-Hour-Ahead Load Forecasting Using Neural Network, IEEE Trans. on Power Systems, 2002, Vol. 17, No. 1, pp. 113-118. https://doi.org/10.1109/59.982201
- Shin et al., A volatility analysis of Korean energy consumption, Economic Study, 2015, Vol. 63, pp. 71-119.
- Shmueli, G. and Lichtendahl Jr., C., Practical Time Series Forecasting with R : A Hands-On Guide, 2nd Edition, Axelrod schnall publishers, 2018.
- Sohn, K., Kim, S., and Shon, E., Fuzzy time series models with triangular fuzzy numbers as parameters, Journal of Korean Data Analysis Society, 2001, Vol. 3, No. 2, pp. 149-162.
- Song, J., Seo, S., Yun, S., Kim, Y., and Cho, C., A study on the energy profile analysis and the forecasting method of the retail shop, in the Proceedings of Korean Communication Society of 2016, pp. 1117-1118.