Predicting Thailand Electricity Load Demand with Modified Fourier Series and Seasonal-Trend Decomposition Methods Using LOESS Transformation
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Keywords
[1] A. C. Şerban and M. D. Lytras, “Artificial intelligence for smart renewable energy sector in europe—smart energy infrastructures for next generation smart cities,” IEEE Access, vol. 8, pp. 77364–77377, 2020, doi: 10.1109/ACCESS. 2020.2990123.
[2] N. Saengngam and U. Thonggumnead, “Predicting the medium-term electricity load demand of Thailand using the generalized estimating equation and the linear mixed effect model,” in 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2015, pp. 1–5, doi: 10.1109/ ECTICon.2015.7206994.
[3] C. Cecati, J. Kolbusz, P. Różycki, P. Siano, and B. M. Wilamowski, “A novel RBF training algorithm for short-term electric load forecasting and comparative studies,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6519–6529, Oct. 2015, doi: 10.1109/ TIE.2015.2424399.
[4] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, and J. He, “Short-term load forecasting with deep residual networks,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3943–3952, Jul. 2019, doi: 10.1109/TSG.2018.2844307.
[5] W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019, doi: 10.1109/TSG.2017.2753802.
[6] S.-M. Baek, “Mid-term load pattern forecasting with recurrent artificial neural network,” IEEE Access, vol. 7, pp. 172830–172838, 2019, doi: 10.1109/ACCESS.2019.2957072.
[7] W. Ahmed, H. Ansari, B. Khan, Z. Ullah, S. M. Ali, C. A. Mehmood, M. B. Qureshi, I. Hussain, M. Jawad, M. U. S. Khan, A. Ullah, and R. Nawaz, “Machine learning based energy management model for smart grid and renewable energy districts,” IEEE Access, vol. 8, pp. 185059–185078, 2020, doi: 10.1109/ACCESS.2020.3029943.
[8] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. California: Holden Day, 1970.
[9] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control. New Jersey: John Willey&Sons, 2008.
[10] G. F. Fan, S. Qing, H. Wang, W. C. Hong, and H. J. Li, “Support vector regression model based on empirical mode decomposition and auto regression for electric load forecasting,” Energies, vol. 6, no 4, pp. 1887–1901, Apr. 2013, doi: 10.3390/ en6041887.
[11] M. W. Li, J. Geng, W. C. Hong, and L. D. Zhang, “Periodogram estimation based on LSSVRCCPSO compensation for forecasting ship motion,” Nonlinear Dynamics, vol. 97, no. 4, pp. 2579–2594, Sep. 2019, doi: 10.1007/s11071- 019-05149-5.
[12] M. W. Li, Y. T. Wang, J. Geng, and W. C. Hong, “Chaos cloud quantum bat hybrid optimization algorithm,” Nonlinear Dynamics, vol. 103, no. 1, pp. 1167–1193, Jan. 2021, doi: 10.1007/s11071- 020-06111-6.
[13] P. Bloomfield, Fourier Analysis of Time Series: An Introduction. New York: John Wiley & Sons, 1976.
[14] C. Robert, C. William, and T. Irma, “STL: A seasonal-trend decomposition procedure based on loess,” Journal of official statistics, vol. 6, no. 1, pp. 3–73, 1990.
[15] J. Osborne, “Notes on the use of data transformations,” Practical Assessment, Research and Evaluation, vol. 8, pp. 1–8. 2002, doi: 10.7275/4vng-5608.
[16] M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied Linear Statistical Models, 5th ed. New York: McGraw Hill, 2005.
[17] I. A. Iwok, “Seasonal modeling of Fourier series with linear trend,” International Journal of Statistics and Robability, vol. 5, no. 6, pp. 65–72, 2016, doi: 10.5539/ijsp.v5n6p65.
[18] S. A. Delurgio, Forecasting Principles and Applications. New York: Mc Graw-Hill, 1998.
[19] A. C. Akpanta and I. S. Iwueze, “On applying the Bartlett transformation to time series data,” Journal of Mathematical Sciences, vol. 20, no. 3, pp. 227–243, 2009.
[20] G. E. Box and D. R. Cox. “An analysis of transformations,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, no. 2, pp. 211–243, 1964, doi: 10.1111/j.2517- 6161.1964.tb00553.x.
[21] H. Arsham and M. Lovric, “Bartlett’s Test” in International Encyclopedia of Statistical Science. M. Lovric, Ed. Berlin, Germany: Springer, 2011, doi: 10.1007/978-3-642-04898-2_132.
[22] F. Bliemel, “Theil’s forecast accuracy coefficient: A clarification” Journal of Marketing Research, vol. 10, no. 44, pp. 444–446, 1973, doi: https:// doi.org/10.2307/3149394.
[23] Energy Policy and Planning office (EPPO), “Electricity Consumption for the Whole Country,” 2021. [Online]. Available: http://www.eppo. go.th/index.php/th/energy-information/staticenergy/ static-electricity
[24] J. Pek, O. Wong, and A. C. Wong, “Data transformations for inference with linear regression: Clarifications and recommendations,” Practical Assessment, Research, and Evaluation, vol. 22, no. 9, 2017, doi: 10.7275/2w3n-0f07.
[25] D. K. Lee, “Data transformation: A focus on the interpretation,” Korean Journal of Anesthesiology, vol. 73, no. 6, pp. 503–508, 2020, doi: 10.4097/kja.20137.
[26] W. S. Cleveland and S. J. Devlin, “Locally weighted regression: An approach to regression analysis by local fitting,” Journal of the American Statistical Association, vol. 83, no. 403, pp. 596–610, 1988, doi: 10.1080/01621459.1988.10478639.
[27] D. R. Gardner, “STL algorithm explained: STL Part II,” 2017. [Online]. Available: http://www. gardner.fyi/blog/STL-Part-II/
[28] V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed. Berlin, Germany: Springer, 2000.
[29] M. Awad and R. Khanna, “Support vector regression,” in Efficient Learning Machines. Apress, California: Berkeley, 2015, pp. 67–80, doi: 10.1007/978-1-4302-5990-9_4.
[30] T. W. MacFarland and J. M. Yates, “Wilcoxon matched-pairs signed-ranks test,” in Introduction to Nonparametric Statistics for the Biological Sciences Using R. Cham, Switzerland: Springer, 2016, pp. 133–175, doi: 10.1007/978-3-319- 30634-6_5.
DOI: 10.14416/j.asep.2022.02.011
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