Forecasting the Electric Energy Supply in Duhok Province using Proposed Methods Based on Wavelet Analysis and Sarima Methods
DOI:
https://doi.org/10.25271/2017.5.2.372Keywords:
SARIMA, Wavelet-SARIMA, De-noising, Time Series Forecasting, ThresholdingAbstract
Many applications have been done in the field of using wavelet analysis for time series analysis. In this study, we used the quarterly data of Electric Energy Supply in Duhok Province-Iraq in Megawatt which represents a sample size (46) observations during the period 2004 and 2015.we aim to describe how wavelet de-noising can be used in time series forecasting and improve the forecasting quality through presenting some proposed methods based on wavelet analysis and SARIMA method and applying on real data and make comparison between methods depending on some statistical criteria.Results from the analysis showed the superiority of the three proposed methods and showed that we can get more information from a series when using Wavelet-SARIMA method and this leads to enhance the classical SARIMA model in forecasting. Furthermore, after many empirical experiments with many wavelet families, it has been found that Daubechies, Coiflets, Discrete Meyer(dmey) and Symlet wavelets are very suitable when denoising the data and out of these four wavelet families, the Daubechies and Discrete Meyer performed better.
References
Aggarwal, S. K., Saini, L. M., and Kumar, A. (2009). Electricity price forecasting using Wavelet-domain and Time domain features in a Regression based technique. International Journal of Recent Trends in Engineering, 2, 33-37.
Al Wadia, S., and Ismail, M., T. (2011). Selecting wavelet transforms model in forecasting financial time series data based on ARIMA model. Applied Mathematical Sciences, 5, 315-326.
Anestis, A., Jeremie, B., and Theofanis, S. (2001). Wavelet Estimators in nonparametric regression: A comparative simulation study. Journal of Statistical Software, 6, 1-83.
Avdakovic, S., Ademovic, A., and Nuhanovic, A. (2012). Insight into the properties of the UK power consumption using a linear regression and wavelet transform approach. Elektrotehniški Vestnik/Electrotechnical Review. 79, 278-283.
Avdakovic, S., Nuhanovic, A., Kusljugic, M., Becirovic, E., and, Turkovic, E. (2013). Wavelet multiscale analysis of a power system load variance. Turkish Journal of Electrical Engineering & Computer Sciences. 21, 1035-1043.
Bruce, A. G., and Gao, H. Y. (1996). Understanding waveshrik: Variance and Bias Estimation. Journal of Biometrical, 83, 727-745.
Burrus, C. S., Gopinath, R. A., and Gao, H. (1998). Introduction to wavelets and wavelet transform, New Jersey, Prentice whole, Upper Saddle River.
Cascio I. L. (2007). Wavelet analysis and de-noising: New tools for economists, London, Queen Marry Press.
Ergen, B. (2012). Signal and image denoising using wavelet transform. Advances in Wavelet theory and their applications in engineering, physics, and technology. InTech publisher.
Frimpong, E., A., and Okyere, P., Y. (2010). Monthly energy consumption forecasting using wavelet analysis and radial basis function neural network, Journal of Science and Technology, 30, 157-164.
Fugal, D. L. (2009). Conceptual Wavelets in digital signal processing. San Diego, California, space and signals Technical Publishing, LLC.
Gencay, R., Selcuk, F. and Whitcher, B. (2002). An introduction to wavelets and other filtering methods in finance and economics, San Diego, California, Academic Press.
George E. P. B., Gwillym, M. J., and Gregory, C. R. (2008). Time series analysis: Forecasting and control, (4th ed.). John Wiley & Sons, INC.
Hubbard, B., B. (1996). The world according to wavelets: The story of a mathematical technique in the making, Wellesley, Massachusetts, A.K. Peters press L.T.D.
Jakub, J., and Jerzy, M. (2016). Day ahead electric power load forecasting by WT-ANN. Przeglad Elektrotechniczny. 4, 152-154.
Khandelwal, I., Adhikari, R., and Verma, G. (2015). Time series forecasting using hybrid ARIMA and ANN models based on DWT Decomposition. Procedia Computer Science. 48, 173–179.
Li, S., Wang, P., and Goe, L. (2014). Electric Load Forecasting Using Wavelet Transform and Extreme Learning Machine. Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning. Bruges (Belgium), 23-25 April.
Makridakis, S., Wheelwright S. C., and Hyndman R. J. (1998). Forecasting: Methods and applications. (3rd ed.). John Wiley & Sons, INC.
Meng, M., Niu, D., and Sun, W. (2011). Forecasting monthly electric energy consumption using feature extraction. Energies. 4, 1495-1507.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J., M. (1996). Wavelet toolbox user`s guide, Version1, USA, The Math Works, Inc.
Moreno-Chaparro, C., Salcedo-Lagos, J., Rivas, E. y., and Orjuela-Canon, A. (2011). A method for the monthly electricity demand to forecast in Colombia based on wavelet analysis and a nonlinear autoregressive model. Ingeniería, 16, 94 – 106.
Mustafa, Q., and Alzubaydi, T., H. (2013). Comparing the Box-Jenkins models before and after the wavelet filtering in terms of reducing the orders with application, Journal of Concrete and Applicable Mathematics, USA, Eudox Press, LCC, 11, 190-198.
Pallavi, L. P., and Raskar, V. P. (2015). Image de-noising with wavelet thresholding method for different level of decomposition. International Journal of Engineering Research and General Science, 3, 1092-1099.
Rana, M., and Koprinska, I. (2016). Forecasting Electricity Load with Advanced Wavelet Neural Networks. Neurocomputing, 182, 118-132.
Vijay, S. C., and Anil, K. T. (2013). Design methodology of a new wavelet basis function for fetal phono cardiographic signals. The Scientific World Journal, 2013, 1-12.
Yaffee, R. and McGee, M. (1999). Introduction to time series analysis and forecasting: With applications of SAS and SPSS. New York, Academic Press.
Yi, M., M., Linn, K., S., and Kyaw, M. (2008). Implementation of Neural Network Based Electricity Load Forecasting. World Academy of Science, Engineering, and Technology, 42, 381-386.
Al Wadia, S., and Ismail, M., T. (2011). Selecting wavelet transforms model in forecasting financial time series data based on ARIMA model. Applied Mathematical Sciences, 5, 315-326.
Anestis, A., Jeremie, B., and Theofanis, S. (2001). Wavelet Estimators in nonparametric regression: A comparative simulation study. Journal of Statistical Software, 6, 1-83.
Avdakovic, S., Ademovic, A., and Nuhanovic, A. (2012). Insight into the properties of the UK power consumption using a linear regression and wavelet transform approach. Elektrotehniški Vestnik/Electrotechnical Review. 79, 278-283.
Avdakovic, S., Nuhanovic, A., Kusljugic, M., Becirovic, E., and, Turkovic, E. (2013). Wavelet multiscale analysis of a power system load variance. Turkish Journal of Electrical Engineering & Computer Sciences. 21, 1035-1043.
Bruce, A. G., and Gao, H. Y. (1996). Understanding waveshrik: Variance and Bias Estimation. Journal of Biometrical, 83, 727-745.
Burrus, C. S., Gopinath, R. A., and Gao, H. (1998). Introduction to wavelets and wavelet transform, New Jersey, Prentice whole, Upper Saddle River.
Cascio I. L. (2007). Wavelet analysis and de-noising: New tools for economists, London, Queen Marry Press.
Ergen, B. (2012). Signal and image denoising using wavelet transform. Advances in Wavelet theory and their applications in engineering, physics, and technology. InTech publisher.
Frimpong, E., A., and Okyere, P., Y. (2010). Monthly energy consumption forecasting using wavelet analysis and radial basis function neural network, Journal of Science and Technology, 30, 157-164.
Fugal, D. L. (2009). Conceptual Wavelets in digital signal processing. San Diego, California, space and signals Technical Publishing, LLC.
Gencay, R., Selcuk, F. and Whitcher, B. (2002). An introduction to wavelets and other filtering methods in finance and economics, San Diego, California, Academic Press.
George E. P. B., Gwillym, M. J., and Gregory, C. R. (2008). Time series analysis: Forecasting and control, (4th ed.). John Wiley & Sons, INC.
Hubbard, B., B. (1996). The world according to wavelets: The story of a mathematical technique in the making, Wellesley, Massachusetts, A.K. Peters press L.T.D.
Jakub, J., and Jerzy, M. (2016). Day ahead electric power load forecasting by WT-ANN. Przeglad Elektrotechniczny. 4, 152-154.
Khandelwal, I., Adhikari, R., and Verma, G. (2015). Time series forecasting using hybrid ARIMA and ANN models based on DWT Decomposition. Procedia Computer Science. 48, 173–179.
Li, S., Wang, P., and Goe, L. (2014). Electric Load Forecasting Using Wavelet Transform and Extreme Learning Machine. Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning. Bruges (Belgium), 23-25 April.
Makridakis, S., Wheelwright S. C., and Hyndman R. J. (1998). Forecasting: Methods and applications. (3rd ed.). John Wiley & Sons, INC.
Meng, M., Niu, D., and Sun, W. (2011). Forecasting monthly electric energy consumption using feature extraction. Energies. 4, 1495-1507.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J., M. (1996). Wavelet toolbox user`s guide, Version1, USA, The Math Works, Inc.
Moreno-Chaparro, C., Salcedo-Lagos, J., Rivas, E. y., and Orjuela-Canon, A. (2011). A method for the monthly electricity demand to forecast in Colombia based on wavelet analysis and a nonlinear autoregressive model. Ingeniería, 16, 94 – 106.
Mustafa, Q., and Alzubaydi, T., H. (2013). Comparing the Box-Jenkins models before and after the wavelet filtering in terms of reducing the orders with application, Journal of Concrete and Applicable Mathematics, USA, Eudox Press, LCC, 11, 190-198.
Pallavi, L. P., and Raskar, V. P. (2015). Image de-noising with wavelet thresholding method for different level of decomposition. International Journal of Engineering Research and General Science, 3, 1092-1099.
Rana, M., and Koprinska, I. (2016). Forecasting Electricity Load with Advanced Wavelet Neural Networks. Neurocomputing, 182, 118-132.
Vijay, S. C., and Anil, K. T. (2013). Design methodology of a new wavelet basis function for fetal phono cardiographic signals. The Scientific World Journal, 2013, 1-12.
Yaffee, R. and McGee, M. (1999). Introduction to time series analysis and forecasting: With applications of SAS and SPSS. New York, Academic Press.
Yi, M., M., Linn, K., S., and Kyaw, M. (2008). Implementation of Neural Network Based Electricity Load Forecasting. World Academy of Science, Engineering, and Technology, 42, 381-386.
Downloads
Published
2017-06-30
How to Cite
Abdulqader, Q. M. (2017). Forecasting the Electric Energy Supply in Duhok Province using Proposed Methods Based on Wavelet Analysis and Sarima Methods. Science Journal of University of Zakho, 5(2), 221–227. https://doi.org/10.25271/2017.5.2.372
Issue
Section
Science Journal of University of Zakho
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.
