Australian Journal of Basic and Applied Sciences, 5(6): 570-580, 2011 ISSN 1991-8178 The Effects of Pre-Processing Methods on Forecasting Improvement of Artificial Neural Networks A. Azadeh, M. Sheikhalishahi, M. Tabesh, A. Negahban Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran Abstract: Along other methods, Intelligent Methods can be used in order to model the trend of changes of a certain variable. These methods require data to be preprocessed before being used in the forecasting process. Generally, the preprocessing step includes omitting outliers, assessment of the missing data, data smoothing, etc. In this paper, the effect of various smoothing methods on the final forecasted results is studied. Furthermore, data from the electricity consumption in Iran over the past 20 years were used as actual data. After being smoothed, these data are then incorporated into an Artificial Neural Network in order to forecast the electrical consumption. The comparisons between several Seasonal Decomposition, including Seasonal Adjustment Series (SAS) and Seasonal Trend Cycle (STC), Exponential Smoothing (Simple, Linear, Holt and Winter) and Box- Jenkins (Moving Average, Auto Regression, and Auto Regression Integrated Moving Average) methods show the superiority of SAS in Decomposition categorization over other methods. The structure of this study may be used for other data sets for improvement of data pre-processing. Key words: Artificial Neural Network; Pre-Processing; Forecasting; Improvement INTRODUCTION It is a difficult task to forecast the electrical consumption due to its various seasonal and monthly changes. Electrical consumption represents two important attributes: on one hand, it shows monthly and seasonal changes and on the other hand it shows an increasing trend (Fig. 1). Fig. 1: The electricity consumption trend in a period of 132 months. Corresponding Author: A. Azadeh, Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran Tel: +9821 88021067, Fax: +9821 82084194. E~mail: aazadeh@ut.ac.ir or ali@azadeh.com 570