Application of a hybrid model based on multiple linear regression -principle component analysis (MLR-PCA) for electricity export forecasting

Articles in Press

Document Type : Article

Authors

1 PhD Student, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran. Iran

2 Industrial Engineering department, Tarbiat Modares University, Iran

3 Assistant Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

International electricity trade as a strategic commodity plays a prominent role in the foreign trade market of countries. Electricity export forecasting leads to better production planning, supply security, blackouts reduc-tion, and obligations fulfillment. This paper aimed to provide a model for electricity export forecasting. In this regard, electricity consumption in different consumer sectors, gas consumption, population, GDP, and electric-ity prices have been entered into the multiple regression model as predictor variables. Although R2 =.976, F=66.110, and SIG<.05 indicate the model appropriateness, the high correlation between the predictor variables created collinearity. In other words, Tolerance, VIF (variance inflation factor), Eigenvalue and the Condition Index are less than .2, more than 10, close to zero, and more than 15 respectively. To solve this problem, two hybrid methods of Multiple Regression-First Difference Function and Multiple Regression-PCA have been used. In the first hybrid method (R2 =.553) the Tolerance and VIF index still show the presence of collinearity. In the second hybrid method (R2 =.936, F=169.9, SIG<.05) due to all the mentioned indicators, the collinearity has been completely resolved. So, the MLR-PCA method is the most appropriate model for electricity export forecasting. The data collected from Iran have been used to illustrate the model.

Keywords

Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 26 December 2022

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History

  • Receive Date: 13 March 2022
  • Revise Date: 10 August 2022
  • Accept Date: 26 December 2022

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