A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques

Abstract

The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required ofelectrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have beenproposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN andNeuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models(ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated movingaverage) is one of the statistical models that utilized in time series prediction during the last several decades. NeuroFuzzyModeling was used to build the prediction system, which give effective in improving the predict operationefficiency. To train the prediction system, a historical data were used. The data representing the monthly electricconsumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposedmodel and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give theefficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method comparedwith the traditional ARIMA method.