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Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search

Electricity demand patterns have many variables related to uncertainty behaviour such as gross domestic product, population, import and export. The characteristics of these variables lead to two problems in forecasting the electricity demand. The first problem is the fitness evaluation in the electr...

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Main Author: Wahab, Musa
Format: Thesis
Language:English
English
Published: 2014
Subjects:
QA76 Computer software
Online Access:https://etd.uum.edu.my/4393/1/s91653.pdf
https://etd.uum.edu.my/4393/2/s91653_abstract.pdf
https://etd.uum.edu.my/4393/
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spelling my.uum.etd.43932023-01-17T07:53:59Z https://etd.uum.edu.my/4393/ Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search Wahab, Musa QA76 Computer software Electricity demand patterns have many variables related to uncertainty behaviour such as gross domestic product, population, import and export. The characteristics of these variables lead to two problems in forecasting the electricity demand. The first problem is the fitness evaluation in the electricity demand forecasting model in which more than one variable are included which leads to increase the sum of squared deviations. The second problem is the use of a single algorithm that failed to solve local optima. These problems resulted in estimation errors and high computational cost. Hybrid genetic algorithm (GA) and Nelder-Mead local search mode 1 has been used to minimize demand estimation errors. However, hybrid GA and Nelder-Mead local search failed to reach the global optimum solution and involve high number of iteration. Hence, an electricity demand forecasting model that reflects the characteristics of electricity demand has been developed in this research. The model is known as the hybrid Real-Value GA and Extended Nelder-Mead (RVGA-ENM). The GA has been enhanced to accept real value while the Nelder-Mead local search is extended to assist in overcoming the local optima problem. The actual electricity demand data of Turkey and Indonesia were used in the experiments to evaluate the performance of the proposed model. Results of the proposed model were compared to the hybrid GA and Nelder-Mead local search, Real Code Genetic Algorithm and Particle Swarm Optimisation. The findings indicate that the proposed model produced higher accuracy for electricity demand estimation. The proposed RVGA-ENM model can be used to assist decision-makers in forecasting electricity demand. 2014 Thesis NonPeerReviewed text en https://etd.uum.edu.my/4393/1/s91653.pdf text en https://etd.uum.edu.my/4393/2/s91653_abstract.pdf Wahab, Musa (2014) Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search. PhD. thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Wahab, Musa
Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
description Electricity demand patterns have many variables related to uncertainty behaviour such as gross domestic product, population, import and export. The characteristics of these variables lead to two problems in forecasting the electricity demand. The first problem is the fitness evaluation in the electricity demand forecasting model in which more than one variable are included which leads to increase the sum of squared deviations. The second problem is the use of a single algorithm that failed to solve local optima. These problems resulted in estimation errors and high computational cost. Hybrid genetic algorithm (GA) and Nelder-Mead local search mode 1 has been used to minimize demand estimation errors. However, hybrid GA and Nelder-Mead local search failed to reach the global optimum solution and involve high number of iteration. Hence, an electricity demand forecasting model that reflects the characteristics of electricity demand has been developed in this research. The model is known as the hybrid Real-Value GA and Extended Nelder-Mead (RVGA-ENM). The GA has been enhanced to accept real value while the Nelder-Mead local search is extended to assist in overcoming the local optima problem. The actual electricity demand data of Turkey and Indonesia were used in the experiments to evaluate the performance of the proposed model. Results of the proposed model were compared to the hybrid GA and Nelder-Mead local search, Real Code Genetic Algorithm and Particle Swarm Optimisation. The findings indicate that the proposed model produced higher accuracy for electricity demand estimation. The proposed RVGA-ENM model can be used to assist decision-makers in forecasting electricity demand.
format Thesis
author Wahab, Musa
author_facet Wahab, Musa
author_sort Wahab, Musa
title Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
title_short Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
title_full Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
title_fullStr Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
title_full_unstemmed Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search
title_sort electricity demand forecasting in turkey and indonesia using linear and nonlinear models based on real-value genetic algorithm and extended nelder-mead local search
publishDate 2014
url https://etd.uum.edu.my/4393/1/s91653.pdf
https://etd.uum.edu.my/4393/2/s91653_abstract.pdf
https://etd.uum.edu.my/4393/
_version_ 1755875162933166080
score 13.211869

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