Figure 2.1. Multi-layer neural network.
Figure 3.1. Time series plot of daily peak load.
Figure 3.2. Time series plot of daily peak load on March 2017.
Figure 3.3. Daily peak load forecast of ARIMA.
Figure 3.4. Daily peak load forecast of ARIMAX.
Figure 3.5. Daily peak load forecast of TBATS.
Figure 3.6. Daily peak load forecast of NNETAR.
Table 3.1. Parameter estimate of ARIMA
Table 3.2. Parameter estimate of ARIMAX
Table 3.3. Parameter estimate of TBATS
Table 3.4. Forecast accuracy of models
참고문헌
- Amjady, N. (2001). Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Transactions on Power Systems, 16, 498-505. https://doi.org/10.1109/59.932287
- Baek, J. K. and Han, J. H. (2015). A study on calibrating the forecasted load of electric power considering special day factor, Journal of Industrial Economics and Business, 28, 191-203.
- Bell, W. R. and Hillmer, S. C. (1983). Modeling time series with calendar variation, Journal of the American statistical Association, 78, 526-534. https://doi.org/10.1080/01621459.1983.10478005
- Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed), John Wiley & Sons, New York.
- De Livera, A. M., Hyndman, R. J., and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106, 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
- Fan, S. and Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model, IEEE Transactions on Power Systems, 27, 134-141. https://doi.org/10.1109/TPWRS.2011.2162082
- Han, J. H. and Baek, J. K. (2010). The load forecasting in summer considering day factor, Journal of the Korea Academia-Industrial Cooperation Society, 11, 2793-2800. https://doi.org/10.5762/KAIS.2010.11.8.2793
- Hong, T., Gui, M., Baran, M. E., and Willis, H. L. (2010). Modeling and forecasting hourly electric load by multiple linear regression with interactions, In Power and Energy Society General Meeting, 2010 IEEE, 1-8.
- Huang, S. J. and Shih, K. R. (2003). Short-term load forecasting via ARMA model identification including non-Gaussian process considerations, IEEE Transactions on Power Systems, 18, 673-679. https://doi.org/10.1109/TPWRS.2003.811010
- Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., and Yasmeen, F. (2018). forecast: Forecasting functions for time series and linear models . R package version 8.4. http://pkg.robjhyndman.com/forecast
- Ji, P. S., Kim, S. K., and Lim, J. Y. (2013). Development of daily peak power demand forecasting algorithm using ELM, The Transactions of the Korean Institute of Electrical Engineers P, 62, 169-174. https://doi.org/10.5370/KIEEP.2013.62.4.169
- Jung, S. W. and Kim, S. (2014). Electricity demand forecasting for daily peak load with seasonality and temperature effects, The Korean Journal of Applied Statistics, 27, 843-853. https://doi.org/10.5351/KJAS.2014.27.5.843
- Lee, J. S., Sohn, H. G., and Kim, S. (2013). Daily peak load forecasting for electricity demand by time series models, The Korean Journal of Applied Statistics, 26, 349-360. https://doi.org/10.5351/KJAS.2013.26.2.349
- Sigauke, C. and Chikobvu, D. (2011). Prediction of daily peak electricity demand in South Africa using volatility forecasting models, Energy Economics, 33, 882-888. https://doi.org/10.1016/j.eneco.2011.02.013
- Sohn, H., Jung, S., and Kim, S. (2016). A study on electricity demand forecasting based on time series clustering in smart grid, The Korean Journal of Applied Statistics, 29, 190-203.
- Song, K. B., Kwon, O. S., and Park, J. D. (2013). Optimal coefficient selection of exponential smoothing model in short term load forecasting on weekdays, The Transactions of the Korean Institute of Electrical Engineers, 62, 149-154. https://doi.org/10.5370/KIEE.2013.62.2.149
- Tak, H., Kim, T., Cho, H. G., and Kim, H. (2016). A new prediction model for power consumption with local weather information, Journal of the Korea Contents Association, 16, 488-498.
- Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting, European Journal of Operational Research, 204, 139-152. https://doi.org/10.1016/j.ejor.2009.10.003
- Taylor, J. W., De Menezes, L. M., and McSharry, P. E. (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead, International Journal of Forecasting, 22, 1-16. https://doi.org/10.1016/j.ijforecast.2005.06.006