Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

Sözen, Adnan; Arcaklıoğlu, Erol

doi:10.1016/j.enpol.2007.04.029

Cited by 116 publications

(35 citation statements)

References 23 publications

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“…In contrast, because an economy's size, as measured by its GDP, may be the most important datum in modern economics, it must have a correlation with a country's diversification degree [11]. It is obvious that countries with large GDP always produce and export more diversified products and that countries with small GDP usually have more homogenous products and markets [12][13][14]. This observation can be described quantitatively by an S-shaped curve that models a country's logarithmic GDP and ex- * zhangjiang@bnu.edu.cn port diversity [1,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Capabilities' substitutability and the “S” curve of export diversity

Lei

Zhang

2014

EPL

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Product diversity, which is highly important in economic systems, has been highlighted by recent studies on international trade. We found an empirical pattern, designated as the "S-shaped curve", that models the relationship between economic size (logarithmic GDP) and export diversity (the number of varieties of export products) on the detailed international trade data. As the economic size of a country begins to increase, its export diversity initially increases in an exponential manner, but overtime, this diversity growth slows and eventually reaches an upper limit. The interdependence between size and diversity takes the shape of an S-shaped curve that can be fitted by a logistic equation. To explain this phenomenon, we introduce a new parameter called "substitutability" into the list of capabilities or factors of products in the tri-partite network model (i.e., the countrycapability-product model) of Hidalgo et al. As we observe, when the substitutability is zero, the model returns to Hidalgo's original model but failed to reproduce the S-shaped curve. However, in a plot of data, the data increasingly resembles an the S-shaped curve as the substitutability expands. Therefore, the diversity ceiling effect can be explained by the substitutability of different capabilities.

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Section: Introductionmentioning

confidence: 99%

Capabilities' substitutability and the “S” curve of export diversity

Lei

Zhang

2014

EPL

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“…Economists use neural networks for forecasting, predicting, and managing the energy markets and analysing the trends of supply and demand for different energy sources (Ashhab, 2008;Azadeh et al, 2007Azadeh et al, , 2008Geem & Roper, 2009;Javeed Nizami & Al-Garni, 1995;Kavaklioglu et al, 2009;Sözen, 2009;Sözen & Arcaklioglu, 2007;Yu et al, 2008). In the transport sector, neural networks have been used for transport simulation to reduce the energy demand.…”

Section: Introductionmentioning

confidence: 99%

“…In the transport sector, neural networks have been used for transport simulation to reduce the energy demand. Unsupervised neural networks (a NN method that works with only input data to find clusters) have a great ability to compare different transport systems and predict the best solution under different conditions (Ashhab, 2008;Azadeh et al, 2007Azadeh et al, , 2008Geem & Roper, 2009;Himanen et al, 1998;Javeed Nizami & Al-Garni, 1995;Kavaklioglu et al, 2009;Sözen, 2009;Sözen & Arcaklioglu, 2007;Yu et al, 2008). NNs have been used in environmental studies to analyse the effects of the use of energy sources on environmental systems.…”

Section: Introductionmentioning

confidence: 99%

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

116

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New Zealand farmers practice a form of ‗industrialised' agriculture that relies on relatively high inputs of fossil fuels, not only to power machinery directly but also for manufacturing artificial fertilizers and agrichemicals (Wells, 2001). Consequently, New Zealand is one of the countries with the highest energy input per unit weight (in agriculture) in the world (Conforti & Giampietro, 1997). Furthermore, in terms of shipping, the influence of increasing global fuel costs is greater on New Zealand farming than in other countries. The main aim of this study was to estimate energy consumption in wheat production. Energy determination can give a clear picture of farms in order to compare different farming systems and energy inputs. The second main target of this study was to develop a neural network model to simulate and predict energy use in wheat production under different conditions incorporating social, geographical, and technical factors. Additionally, the interaction effects between different factors were examined in this study.This study was conducted on irrigated and dryland wheat fields in Canterbury, New Zealand, in the 2007-2008 harvest year. Canterbury represents 87% of the wheat area and 66% of the arable area harvested in New Zealand.Energy consumption here is defined as the energy used for the production of wheat until it leaves the farm. The data were collected from three different sources:questionnaire, literature review, and field measurements. The energy inputs estimated in this study are those that go into on-farm production systems before the post-harvest processes. The study considered only the energy used in wheat production, without taking into account the natural sources of energy (radiation, wind, rain, etc). IVA survey was conducted to collect the most important data and to identify farmers' attitudes and opinions about energy consumption. In this study, 40 arable farms were selected randomly, as far as possible. From the initial analysis, it was found that 30 farms were irrigated and the rest were dryland farms. Irrigated farms were irrigated between one to ten times annually depending on the rainfall. Some irrigated farms have also been converted to dryland farms, or vice versa, in different years. The data for a large number of farming factors were gathered in the survey.Average energy consumption for wheat production was estimated at around 22,600 MJ/ha. On average, fertilizer and electricity (mostly for irrigation) were used more than other energy sources, at around 10,654 MJ/ha (47%) and 4,870 MJ/ha (22%), respectively. The average energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. This study is the first to create an appropriate Artificial Neural Network (ANN) model to predict energy consumption in wheat production with optimum variables. This study would be the first to investigate the factors related to the efficient use of energy in agricultural production. A careful study o...

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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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Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

Cited by 116 publications

References 23 publications

Capabilities' substitutability and the “S” curve of export diversity

Capabilities' substitutability and the “S” curve of export diversity

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Yapay Si̇ni̇r Ağlari İle Türki̇ye Net Enerji̇ Talep Tahmi̇ni̇

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