A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques
The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Inte...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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College of Engineering, University of Basrah
2015-06-01
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| Series: | Iraqi Journal for Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.org/volums/volume11/IJEEE11PDF/paper1112.pdf |
| _version_ | 1828303398260178944 |
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| author | Khadeega Abd Al-zahra Khulood Moosa Basil H. Jasim |
| author_facet | Khadeega Abd Al-zahra Khulood Moosa Basil H. Jasim |
| author_sort | Khadeega Abd Al-zahra |
| collection | DOAJ |
| description | The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of
electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been
proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and
Neuro-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 moving
average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro-
Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation
efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric
consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed
model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the
efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared
with the traditional ARIMA method. |
| first_indexed | 2024-04-13T13:53:24Z |
| format | Article |
| id | doaj.art-647e3b50f72c4587b30ab09af335ff40 |
| institution | Directory Open Access Journal |
| issn | 1814-5892 2078-6069 |
| language | English |
| last_indexed | 2024-04-13T13:53:24Z |
| publishDate | 2015-06-01 |
| publisher | College of Engineering, University of Basrah |
| record_format | Article |
| series | Iraqi Journal for Electrical and Electronic Engineering |
| spelling | doaj.art-647e3b50f72c4587b30ab09af335ff402022-12-22T02:44:17ZengCollege of Engineering, University of BasrahIraqi Journal for Electrical and Electronic Engineering1814-58922078-60692015-06-01111110123A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent TechniquesKhadeega Abd Al-zahra0Khulood Moosa1Basil H. Jasim2Dept. of Geographic, college of Arts,University of Basrah, Basrah, IraqElectrical Engineering Department, Engineering College, University of Basrah, Basrah, Iraq.Electrical Engineering Department, Engineering College, University of Basrah, Basrah, Iraq.The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-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 moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro- Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.http://ijeee.org/volums/volume11/IJEEE11PDF/paper1112.pdfARIMA modelsArtificial neural networksBox-JenkinsNeuro-fuzzy |
| spellingShingle | Khadeega Abd Al-zahra Khulood Moosa Basil H. Jasim A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques Iraqi Journal for Electrical and Electronic Engineering ARIMA models Artificial neural networks Box-Jenkins Neuro-fuzzy |
| title | A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques |
| title_full | A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques |
| title_fullStr | A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques |
| title_full_unstemmed | A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques |
| title_short | A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques |
| title_sort | comparative study of forecasting the electrical demand in basra city using box jenkins and modern intelligent techniques |
| topic | ARIMA models Artificial neural networks Box-Jenkins Neuro-fuzzy |
| url | http://ijeee.org/volums/volume11/IJEEE11PDF/paper1112.pdf |
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