(2) Subanar Subanar (Universitas Gadjah Mada, Indonesia)
(3) Suhartono Suhartono (Institut Teknologi Sepuluh Nopember Surabaya, Indonesia)
(4) Herni Utami (Universitas Gadjah Mada, Indonesia)
*corresponding author
AbstractShort-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
KeywordsHolt-Winter; Hybrid model; Exponential smoothing; Neural network; Electricity
|
DOIhttps://doi.org/10.26555/ijain.v2i3.69 |
Article metricsAbstract views : 3650 | PDF views : 403 |
Cite |
Full TextDownload |
References
M. Bernardi, and L. Petrella, “Multiple seasonal cycles forecasting model: the Italian electricity demand,” Stat Methods Appl, vol. 24, pp. 671-695, 2015.
A. M. De Livera, R.J. Hyndman, and R. D. Snyder, “Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, vol. 106, issue 496, pp. 1513-1527, 2011.
P. G, Gould, A. B. Koehler, J.K. Ord, R. D. Snyder, R.J. Hyndman, and F. Vahid-Araghi, “Forecasting time series with multiple seasonal patterns,” European Journal of Operational Research, vol. 191, pp. 207-222, 2008.
O. B. Shukur, N. M. Fadhil, M. H. Lee, and M. H. Ahmad, “Electricity load forecasting using hybrid of multipilicative double seasonal exponential smoothing model with artificial neural network,” Jurnal Teknologi, vol. 69, issue 2, pp. 65-70, 2014.
Suhartono and A. J. Endharta, “Short term electricity load demand forecasting in Indonesia by using double seasonal recurrent neural network,” International Journal of Mathematical Models and Methods in Applied Sciences, vol. 3, issue 3, pp. 171-178, 2009. http://www.naun.org/main/NAUN/ijmmas/19-096.pdf
J. W. Taylor, “Short-term electricity demand forecasting using double seasonal exponential smoothing,” Journal of the Operational Research Society, vol. 54, pp. 799-805, 2003.
P. R. Winter, “Forecasting sales by exponentially weighted moving averages,” Management Science, vol. 6, issue 3, pp. 324-342, 1960.
R. J. Hyndman, A. B. Koehler, J.K. Ord, and R. D. Snyder, Forecasting with exponential smoothing: the state space approach. Springer-Verlag, Berlin, 2008.
J.K. Ord, A. B. Koehler, and R.D. Snyder, “Estimation and prediction for a class of dynamic nonlinear statistical models,” Journal of the American Statistical Association, vol. 92, issue 440, pp. 1621-1629, 1997.
R.J. Hyndman, A. B. Koehler, R.D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” International Journal of forecasting, vol. 18, issue 3, 439-454, 2002.
J. W. Taylor, and R. D. Snyder, Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing. Technical Report 09/09. Department of Econometrics and Business Statistics, Monash University, 2009.
A. Azadeh, S.F. Ghaderi, M. Sheikhalishahi, and B.P. Nokhandan, “Optimization of short load forecasting in electricity market of Iran using artificial neural networks,” Optimization and Engineering, vol. 15, issue 2, pp. 485-508, 2014.
A. I. Melhum, L. Omar, and S.A. Mahmood, “Short term load forecasting using artificial neural network,” International Journal of Soft Computing and Engineering (IJCSE), vol. 3, issue 1, pp. 56-58, 2013.
C. A. Moturi, and F. K. Kioko, F. K, “Use of artificial neural networks for short-term electricity load forecasting of Kenya National grid power system,” International Journal of Computer Applications, vol. 63, issue 2, pp. 25-30, 2013.
A. Shrivastava, and A. Bhandakkar, “Short term load forecasting using artificial neural network techniques,” International Journal of Engineering Research and Applications, vol. 3, issue 5, pp. 1524-1527, 2013.
S. Simsar, M. Alborzi, J. Nazemi, and M. A. Layyegh, “Forecasting power demand using neural networks model,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 2, issue 5, 441-446, 2013.
J. W. Taylor, L. M. de Menezes, and P. E. McSharry, “A comparison of univariate methods for forecasting electricity demand up to a day ahead,” International Journal of Forecasting, vol. 22, pp. 1-16, 2006.
H.J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search,” Electrical Power and Energy System, vol. 62, pp. 118-129, 2014.
W. Lee, and J. Hong, “A hybrid dynamic and fuzzy time series model for mid-term power load forecasting,” Electrical Power and Energy Systems, vol. 64, pp. 1057-1062, 2015.
S. Gupta, V. Singh, A.P. Mittal, and A. Rani, “A hybrid modell of wavelet and neural network for short term load forecasting,” International Journal of Electronic and Electrical Engineering, vol. 7, issue 4, pp. 387-394, 2014.
C.C. Holt, “Forecasting seasonals and trends by exponentially weighted moving averages,” International Journal of Forecasting, vol. 20, pp. 5-10, 2004
A. Harvey, S. J. Koopman, and M. Riani, “The modelling and seasonal adjustment of weekly observations,” Journal of Business & Economic Statistics, vol. 15, issue 3, 354-368, 1997.
A. Harvey, Forecasting structural time series models and the Kalman filter, Cambridge University Press, 1989.
A. Lapedes, R. Farber, Nonlinear signal processing using neural networks: prediction and system modelling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM, 1978.
A. Lapedes, and R. Farber, How neural nets work. In: Anderson, D. Z., (Ed.), Neural Information Processing Systems, American Institute of Physics, New York, , 1988, pp. 442-456.
G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: The state of the art,” International Journal of Forecasting, vol. 14, pp. 35-62, 1998.
S. Sapna, A. Tailarasi, and M. P. Kumar, “Backpropagation learning algorithm based on Levenberg Marquardt Algorithm,” Computer Science & Information Technology, vol. 2, pp. 393-398, 2012. http://airccj.org/CSCP/vol2/csit2438.pdf
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0