2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 1 , PP: 08-18, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Abdulsalam Elnaeem Balila 1 * , Ani Bin Shabri 2
Doi: https://doi.org/10.54216/FPA.140101
Accurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA) denoted by (EEMD+ARIMA), to know which model is better performing a historical US monthly electricity consumption from DEC-2000 to SEP-2022 were used. The data were divided into training set (90%) and testing set (10%) to insure the model accuracy. The mean absolute square error, root mean square error, mean absolute error and mean absolute percentage error measurements were used to test the ARIMA and hybrid EEMD+ARIMA performance, the results show that the hybrid EEMD+ARIMA outperforms ARIMA model with the lowest RMSE, MAE, MPE, MAPE, MASE. For the best model, Akaike Information Criterion and Bayesian Information Criterion were applied to choose the best. The results show that the AIC and BIC of the EEMD+ARIMA were lower than the ARIMA model, which indicates that the EEMD+ARIMA is better than the single ARIMA in forecasting of electricity consumption. The conclusion reveals that the hybrid EEMD+ARIMA provides more accurate forecasting and performs significantly better than the ARIMA in forecasting of electricity.
EEMD , SINGLE ARIMA , IMFS , HYBRID EEMD+ARIMA , FORECASTING.
[1] A. D. M. T. M. M. O. A. a. N. G. W. Gao, "Different states of multi-block based forecast engine for price and load prediction," International Journal of Electrical Power & Energy Systems, 2019.
[2] B. S. C. e. a. C. Liu, "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, 2020.
[3] Zhi-Yu Wang, Jun Qiu, and Fang-Fang Li, "Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting," MDPI, Vols. 2018, 10, 853, 2018.
[4] Yu-Sheng Kao , Kazumitsu Nawata, Chi-Yo Huang, "Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition," 2020.
[5] Guorong Zhu, Sha Peng, Yongchang Lao, Qichao Su, Qiujie Sun, "Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method," Hindawi, Vols. Volume 2021, Article ID 6613604, p. 9 pages, 2021.
[6] Z. S. S. R. L. M. C. W. H. H. S. Q. Z. N.-C. Y. C. C. T. a. H. H. L. Norden E. Huang, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," vol. 454, no. 1971, 1998.
[7] F.-J. LIN, "ADDING EMD PROCESS AND FILTERING ANALYSIS TO ENHANCE PERFORMANCES OF ARIMA MODEL WHEN TIME SERIES IS MEASUREMENT DATA," Romanian Journal of Economic Forecasting, 2015.
[8] Zilrahmi Zilrahmi, Hari Wijayanto, Farit M. Afendi, Rizal Bakri, "COMPARISON OF EEMD AND MODIFIED EEMD METHOD IN PREDICTING OF CURLY RED CHILI PRICE IN INDONESIA*," Indonesian Journal of Statistics and Its Applications, Vols. 4 No 2 (2020), 374 - 381, 2020.
[9] J. W. G. Z. Y. D. Yuanyuan Wang, "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," 2012.
[10] Sheng-weiFei, "A hybrid model of EMD and multiple-kernel RVR algorithm for wind speed prediction," vol. 78, 2016.
[11] Muzi Zhang, Junyi Li, Bing Pan, and Gaojun Zhang, "Weekly Hotel Occupancy Forecasting of a Tourism Destination," MDPI, Vols. 2018, 10, 4351, p. 17, 2018.
[12] Xiaoli Qiang, Muhammad Aamir, Muhammad Naeem, Shaukat Ali, "Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model," Tech Science Press, 2021.
[13] Zhang, G., Wu, J., Pan, B., Li, J., Ma, M., & Wang, J, "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model," Tourism Economics Journal, Vols. 23, 7: pp. 1496-1514, 2017.
[14] W. X. J. D. Y. Z. Jujie Wang, "Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning," 2022.
[15] K. N. C.-Y. H. Y. Kao, "Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition," Taiwan.
[16] U.S. Energy Information Administration, "U.S. Energy Information Administration," 01 12 2022. [Online]. Available: https://www.eia.gov/electricity/data/browser/#/topic/2?agg=2,0,1&fuel=vvg&geo=g&sec=g&freq=M&start=200101&end=202210&ctype=linechart<ype=pin&rtype=s&maptype=0&rse=0&pin=.
[17] A. F. Siegel, Practical Business Statistics, Washington: sciencedirect, 2011.
[18] W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods, California: Addison-Wesley, 1990.
[19] Z. Zhang, Mathematical and Physical Fundamentals of Climate Change, Beijing, China: Elsevier, 2015.
[20] T. Anderson, The Statistical Analysis of Time Series, New York: John Wiley & Sons, Inc, 1971.
[21] G. a. J. G. Box, Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day, 1976.
[22] N. E. H. Zhaohua Wu, "ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD," worldscientific, pp. 1-41, 2009.
[23] Y.-L. J. Y.-N. Li-Hong Tang, "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," vol. 141, 2020.
[24] H. Akaike, "A New Look at the Statistical Model Identification," vol. 19, no. 6, 1974.
[25] G. Schwarz, "Estimating the Dimension of a Model," vol. 6, 1978.
