Artificial neural network approach for load forecasting of Iraqi super grid
Dissertant
Thesis advisor
University
University of Technology
Faculty
-
Department
Department of Electrical Engineering
University Country
Iraq
Degree
Master
Degree Date
2013
English Abstract
Load Forecast is a basic goal in electrical power system.
Optimal operation, maintenance, and planning are based on a good prediction of the electrical load.
Higher error of electrical loads estimation may lead to problems in this system.
Therefore, the choice of low accuracy model gives critical results to the power system.
In this work three programs were built, these are the load forecasting program using the multiple linear regression (MLR) method, and two Artificial Neural Network programs.
All programs were written in MATLAB environment and have been developed to solve the load forecasting schedule of Iraqi super grid network.
The usefulness of the regression method has been tested on the Canadian power system and the results were compared to those of the published papers and the same program is then applied on the Iraqi super grid network to obtain the forecasted load for the years 2012 and 2013; the results were compared to the data taken from Iraqi National dispatch Center (INDC).
All the calculations were done for two seasons, Summer and Winter seasons.
To demonstrate the effectiveness of the Artificial Neural Network (ANN), the programs of the ANN were applied to forecast the load for the Iraqi power system.
Three-layered ANN paradigm with Levenberg-Marquardt back-propagation algorithm has been used.
The first program was applied to forecast the load of the year 2013 and the results were compared to those obtained from multiple linear regression program.
The second ANN program was applied to forecast the load of the years 2014 and 2015 and the results are reported to guide forecasting future needs for Iraqi supper grid network.
The obtained results show that the ANN method takes advantages of accuracy and efficiency in prediction.
Main Subjects
Topics
American Psychological Association (APA)
Kamil, Samarah Majid. (2013). Artificial neural network approach for load forecasting of Iraqi super grid. (Master's theses Theses and Dissertations Master). University of Technology, Iraq
https://search.emarefa.net/detail/BIM-418402
Modern Language Association (MLA)
Kamil, Samarah Majid. Artificial neural network approach for load forecasting of Iraqi super grid. (Master's theses Theses and Dissertations Master). University of Technology. (2013).
https://search.emarefa.net/detail/BIM-418402
American Medical Association (AMA)
Kamil, Samarah Majid. (2013). Artificial neural network approach for load forecasting of Iraqi super grid. (Master's theses Theses and Dissertations Master). University of Technology, Iraq
https://search.emarefa.net/detail/BIM-418402
Language
English
Data Type
Arab Theses
Record ID
BIM-418402
