Browse > Article
http://dx.doi.org/10.5351/KJAS.2019.32.1.161

Forecasting daily peak load by time series model with temperature and special days effect  

Lee, Jin Young (Department of Applied Statistics, Chung-Ang University)
Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.1, 2019 , pp. 161-171 More about this Journal
Abstract
Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.
Keywords
daily peak load; electric load forecasting; special days effect; time series model;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 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.
2 Bell, W. R. and Hillmer, S. C. (1983). Modeling time series with calendar variation, Journal of the American statistical Association, 78, 526-534.   DOI
3 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.
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.
8 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.   DOI
9 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.   DOI
10 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
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 Sigauke, C. and Chikobvu, D. (2011). Prediction of daily peak electricity demand in South Africa using volatility forecasting models, Energy Economics, 33, 882-888.   DOI
15 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.
16 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.   DOI
17 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.
18 Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting, European Journal of Operational Research, 204, 139-152.   DOI
19 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.   DOI