Forecasting the Electric Energy Supply in Duhok Province using Proposed Methods Based on Wavelet Analysis and Sarima Methods

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.

[1]  R. Gencay,et al.  An Introduction to Wavelets and Other Filtering Methods in Finance and Economics , 2001 .

[2]  Wei Sun,et al.  Forecasting Monthly Electric Energy Consumption Using Feature Extraction , 2011 .

[3]  Monnie McGee,et al.  Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS , 2000 .

[4]  Irena Koprinska,et al.  Forecasting electricity load with advanced wavelet neural networks , 2016, Neurocomputing.

[5]  David Jerison.  The World According to Wavelets : The Story of a Mathematical Technique in the Making Reviewed by David Jerison , 1999 .

[6]  D. Lee Fugal,et al.  Conceptual wavelets in digital signal processing : an in-depth, practical approach for the non-mathematician , 2009 .

[7]  L. Pazvakawambwa,et al.  Forecasting methods and applications. , 2013 .

[8]  Ratnadip Adhikari,et al.  Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition , 2015 .

[9]  Jerzy Mikulik,et al.  Day ahead electric power load forecasting by WT-ANN , 2016 .

[10]  Mohd Tahir Ismail,et al.  Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model , 2011 .

[11]  Anil Kumar Tiwari,et al.  Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals , 2013, TheScientificWorldJournal.

[12]  Jean-Michel Poggi,et al.  Wavelet Toolbox User s Guide , 1996 .

[13]  Myint Myint Yi,et al.  Implementation of Neural Network Based Electricity Load Forecasting , 2008 .

[14]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[15]  A. Bruce,et al.  Understanding WaveShrink: Variance and bias estimation , 1996 .

[16]  L. Saini,et al.  Electricity price forecasting using Wavelet domain and Time domain features in a Regression based technique , 2009 .

[17]  I. L. Cascio,et al.  Wavelet Analysis and Denoising: New Tools for Economists , 2007 .

[18]  Song Li,et al.  Electric load forecasting using wavelet transform and extreme learning machine , 2014, ESANN.

[19]  Samir Avdakovic,et al.  Insight into the Properties of the UK Power Consumption Using a Linear Regression and Wavelet Transform Approach , 2013, 1308.5572.

[20]  Anestis Antoniadis,et al.  Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study , 2001 .

[21]  Samir Avdakovic,et al.  Wavelet multiscale analysis of a power system load variance , 2013 .

[22]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[23]  Burhan Ergen,et al.  Signal and Image Denoising Using Wavelet Transform , 2012 .

[24]  Edwin Rivas Trujillo,et al.  A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model , 2011 .

[25]  E. A. Frimpong,et al.  Monthly energy consumption forecasting using Wavelet analysis and radial basis function neural network , 2010 .