Türkiye İçin Anfıs Modeli İle Birincil Enerji Talep Tahmini

Bayramoğlu, Turgut; Pabuçcu, Hakan; Boz, Füsun Çelebi

doi:10.21121/eab.2017328408

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…Various indicators are used, such as growth rate, population, and energy prices as an independent variable. The results show that, Turkey's energy demand has increased as in other OECD countries [3].…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 88%

“…In previous studies, various approaches have been used to model industrial energy demand. These methods are the ARIMA method, Artificial Intelligence Based Prediction Models, Machine Learning (ML) based methods, Econometric models, and simulation models [3][4][5][6].…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

“…Bayramoglu et al [3] used ANFIS to estimate primary energy demand between 2016-2030 in Turkey. Various indicators are used, such as growth rate, population, and energy prices as an independent variable.…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

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Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

2021

AIS

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Energy is one of the most critical inputs in social and economic development, is an essential factor in increasing living standards and creating sustainable development. Since energy is an indispensable input in all sectors, energy dependence contributed to the necessity of countries' energy policy. It has critical importance to predict energy demand to determine energy policies. According to Turkey's annual energy consumption, the industry sector has consumed the most energy in the last five years. The prediction and analysis of energy demand with economic data in the industrial sector is an essential indicator of the economic development relationship between energy demand and industry. In this study, Multiple Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and optimized ANFIS with Particle Swarm Optimization (PSO) methods are employed to forecast energy demand for Turkish industrial sectors. The indicators which affect energy consumption were determined to estimate the energy demand. The 30-year dataset between 1990 and 2019 was split as training and test set. MLR, ANFIS and PSO-ANFIS were compared according to performance evaluation, and the most proper model was identified. The coefficient of determination (R 2 ) for PSO-ANFIS, MLR, and ANFIS models are 0.9951, 0.9889, and 0.9932 in the training stage, and 0.9423, 0.9181, and 0.8776 in the testing stage, respectively. The study results indicated that the PSO-ANFIS model showed superior prediction capability with the least estimation error than MLR and ANFIS models. Consequently, parameters tuned PSO-ANFIS is able to predict the industrial energy demand in Turkey with high accuracy.

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Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 88%

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

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Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

2021

AIS

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The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods

Kayakuş¹

2020

Alphanumeric Journal

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Energy demand and consumption are very important for the development and progress of countries. Energy demand is increasing rapidly day by day, especially in developing countries. Energy policies should be determined correctly to sustain the industry sector and make the right investments. Forecasting energy demand in the near and long term is important for the strategy that countries will follow. In this study, by using the monthly electricity energy data realized in Turkey between January 2016 and March 2020 and other data affecting this, a model to estimate electrical energy consumption was developed. In this model, artificial neural networks (ANN) and support vector regression (SVR) were used as methods. This study used 15 independent variables as the input value, and Turkey's energy consumption value as the dependent variable was estimated. Correlation, coefficient of determination, MAE, MSE, RMSE, and MAPE statistical methods were used to measure success and error rate, and both models were found to have acceptable error values and success estimation rates. According to the results, it was concluded that the ANN method was more successful than the SVR method.

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Anfis Metodu İle CDS Pri̇mi̇ Tahmi̇nlemesi̇: Türki̇ye Örneği̇

Karabiyik¹

2021

International Journal of Management Economics and Business

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Kredi Temerrüt Takası (CDS) primi ülkelerin risk derecelerini ve yatırım yapılabilirliklerini ifade etmektedir. CDS primini doğru tahmin edebilen yatırımcılar fonlarını güvenilir ülkelere ve doğru kaynaklara aktarabilmektedirler. Bu çalışmada Türkiye'nin CDS priminin Uyarlamalı Sinirsel Bulanık Çıkarım Sistemi (ANFIS) yöntemi ile tahmin edilmesi amaçlanmıştır. Çalışmada girdi değişkenleri olarak "döviz kuru", "kredi notu", "faiz oranı", "hisse senedi fiyatı", "hisse senedi volatilitesi" ve "hisse senedi getirisi" değerleri ele alınmıştır. Çalşmadaki tüm veriler günlük olarak elde edilmiş olup, 2015-2020 periyodunu kapsamaktadır. ANFIS girdi seçimi analizi neticesinde CDS priminin tahmin edilmesinde en fazla etkisi olan girdi değişkenlerinin "döviz kuru", "hisse senedi fiyatı" ve "faiz oranı" olduğu bulunmuştur. Söz konusu girdi değişkenlerin yer aldığı eğitim veriseti ile ANFIS modeli eğitilmiş ve modelin daha önce hiç görmediği test veri seti için gelecek CDS primi değerlerine yönelik tahminler elde edilmiştir. Analiz sonucunda CDS primi tahmin değerlerinin gerçek CDS primi değerlerine oldukça yakın olduğu tespit edilmiştir. O halde ANFIS modelinin Türkiye'deki CDS primlerini oldukça iyi bir şekilde tahmin ettiği söylenebilmektedir. Bu çalışmayla CDS primlerinin izleyen dönemlerde alabileceği değerler hakkında yatırımcılara ve politika geliştiricilere bilgi verilmesi amaçlanmaktadır.

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Türkiye İçin Anfıs Modeli İle Birincil Enerji Talep Tahmini

Cited by 10 publications

References 4 publications

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods

Anfis Metodu İle CDS Pri̇mi̇ Tahmi̇nlemesi̇: Türki̇ye Örneği̇

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