Projection of future transport energy demand of Thailand

Limanond, Thirayoot; Jomnonkwao, Sajjakaj; Srikaew, Artit

doi:10.1016/j.enpol.2011.02.045

Cited by 119 publications

(47 citation statements)

References 17 publications

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“…Örneğin; zaman serileri [5] Enerji talep tahmini alanında sıklıkla kullanılan tahmin araçlarından biri de YSA'dır. Sözen ve arkadaşları [13,14] Türkiye'nin net enerji tüketimini,Pao [15] Tayvan'ın elektrik enerji tüketimini, Kavaklıoğlu ve arkadaşları [16] Türkiye'nin elektrik enerji tüketimini, Geem ve Roper [17] Güney Kore'nin enerji talebini, Ekonomou [18] Yunanistan'ın toplam enerji talebini, Limanond ve arkadaşları [19] Tayland'ın ulaştırma enerji talebini, Kankal ve arkadaşları [20]Türkiye'nin genel enerji tüketimini, Geem [21] Güney Kore'nin ulaştırma enerji talebini ve Bilgili ve arkadaşları [22] Türkiye'deki konut ve sanayi sektörlerinin enerji tüketimini tahmin etmek için YSA'nı kullanmışlardır. YSA ile gerçekleştirilen enerji talep tahmin çalışmaları Tablo 1'de özet halinde sunulmuştur.…”

Section: Li̇teratür Araştirmasi (Literature Review)unclassified

Yapay Si̇ni̇r Ağlari İle Türki̇ye Net Enerji̇ Talep Tahmi̇ni̇

Es¹,

Kalender²,

Hamzaçebi³

2014

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖZETBu çalışmada, yapay sinir ağları (YSA) ile Türkiye net enerji talebi tahmin edilmiştir. Türkiye net enerji talebini tahmin etmek için 1970-2010 yılları arasındaki Gayri Safi Yurtiçi Hâsıla (GSYH) , nüfus, ithalat, ihracat, bina yüz ölçümü ve taşıt sayısı değişken verileri YSA modelinin girdisi olarak kullanılmıştır. Kurulan YSA modelinin tahmin performansı, çoklu doğrusal regresyon tekniği ile karşılaştırmalı olarak ortaya konmuştur. FORECASTING THE NET ENERGY DEMAND OF TURKEY BY ARTIFICIAL NEURAL NETWORKS ABSTRACTIn this study, the net energy demand of Turkey has been predicted by artificial neural networks (ANN). In order to forecast net energy demand of Turkey, Gross Domestic Product (GDP), population, import, export, area of the building and vehicles number data was used as input of ANN model. The prediction performance of built ANN model has been demonstrated in comparison with a multiple linear regression technique. The comparisons are shown the superiority of ANN. By using the model which is acceptable and high accuracy, the net energy demand of Turkey has been predicted that between the years of 2011-2025.

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Section: Li̇teratür Araştirmasi (Literature Review)unclassified

Yapay Si̇ni̇r Ağlari İle Türki̇ye Net Enerji̇ Talep Tahmi̇ni̇

Es¹,

Kalender²,

Hamzaçebi³

2014

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…Zhang et al [2] forecasted the transportation energy demand in 2010,2015 and 2020 based on two scenarios. Limanond et al [3] developed log-linear regression models to project the transportation energy consumption in Thailand for 2010-2030.…”

Section: Parametric Techniquesmentioning

confidence: 99%

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Anand¹,

Suganthi²

2017

Preprint

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In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , per capita demand, income and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between GSDP and the electricity demand of the state.

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“…The modeling techniques used so far can be divided into two categories : the parametric techniques and non parametric techniques. Studies using parametric techniques [1,2,3,4,5] Cincotti .S et al [44] have highlighted the usability of computational intelligence for forecasting electricity prices. Among these artificial intelligence based methodology, artificial neural network (ANN) has emerged as one of the most prominent technique that has become very popular with the researchers.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Anand¹,

Suganthi²

2018

Preprint

View full text Add to dashboard Cite

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

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Projection of future transport energy demand of Thailand

Cited by 119 publications

References 17 publications

Yapay Si̇ni̇r Ağlari İle Türki̇ye Net Enerji̇ Talep Tahmi̇ni̇

Yapay Si̇ni̇r Ağlari İle Türki̇ye Net Enerji̇ Talep Tahmi̇ni̇

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

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