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http://dx.doi.org/10.5351/KJAS.2021.34.4.623

Comparison of time series predictions for maximum electric power demand  

Kwon, Sukhui (Department of Information & Statistics, Chungbuk National University)
Kim, Jaehoon (Department of Information & Statistics, Chungbuk National University)
Sohn, SeokMan (Department of Information & Statistics, Chungbuk National University)
Lee, SungDuck (Department of Information & Statistics, Chungbuk National University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.4, 2021 , pp. 623-632 More about this Journal
Abstract
Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.
Keywords
electric power demand; smoothing method; seasonal ARIMA; weighted average model; ARCH; GARCH;
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  • Reference
1 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
2 Box GEP, Jenkins GM, and Reinsel GC (1994). Time Series Analysis: Forecasting and Control, Princeton-Hall International.
3 Park J, Kim YB, and Jung CW (2013). Short-term forecasting of city gas daily demand, Journal of Korean Institute of Industrial Engineers, 39, 247-252.   DOI
4 Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 50, 987-1007.   DOI
5 Jung S and Kim S (2014). Electricity demand forecasting for daily peak load with seasonality and temperature effects, Journal of the Korean Data And Information Science Society, 27, 843-853.
6 Lee JH, Oh SJ, Yoon Y, Ahn YH, Kim JS, Cho WS, and Lee SD (2019). Analysis of time series to support decision making on V2G using energy consumption data, Journal of the Korean Data And Information Science Society, 30, 401-414.   DOI
7 Taylor JW and Buizza R (2003). Using weather ensemble predictions in electricity demand forecasting, International Journal of Forecasting, 19, 57-70.   DOI
8 Winters PR (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6, 324-342.   DOI
9 Kalimoldayev M, Drozdenko A, Koplyk I, Marinich T, Abdildayeva A, and Zhukabayeva T (2020). Analysis of modern approaches for the prediction of electric energy consumption, Open Engineering, 10, 350-361.   DOI