Forecasting and Analysis in Turkish Electricity Market With Multiple Regression, Arima and Artificial Neural Network Methods

ARSLAN, Burak; Ertuğrul, İrfan

doi:10.11611/yead.988146

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…The fact that the coefficient of determination is close to “0” indicates that the independent variables cannot adequately explain the dependent variables. Furthermore, this coefficient is close to “1” indicates that the model is quite good in explaining the dependent variable [ 35 ]. The following formula is used to determine the value.…”

Section: Solution Methodologymentioning

confidence: 95%

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Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Ucar

Aygün

Tanyeri

2023

J Therm Anal Calorim

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As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. The necessity of turbofan with higher energy efficiency has been greatly drawn attention since these are operating dependent to fossil fuels. In this study, energy and emission metrics of fifty-one mixed flow turbofan engines (MFTE) with different bypass ratio, overall pressure ratio and fuel flow are modeled with multi-regression (MR) method. The obtained models are subjected to metaheuristic approaches involving genetic algorithm (GA) and simulated annealing (SA) so as to decrease error of the models. According to MR findings, rated thrust of MFTEs is estimated with 1.4877 of minimum square error (MSE) whereas GA and SA make it lower as 1.3404 and 1.2524, respectively. On the other hand, NO x emission index of MFTEs is predicted with relatively low coefficient of determination (R 2 ) as 0.8620. However, its accuracy is enhanced to 0.8633 (with GA) and 0.8655 (with SA). Finally, exergy efficiency of MFTEs is estimated the highest model correctness with GA. Namely, R 2 of the model is computed as 0.9280 with GA and 0.9277 with SA. Without applying these methods, its R 2 is obtained as 0.9263 with MR. When considering these outcomes, thanks to modeling and optimization methods, prediction of performance and emission indexes of mixed flow turbofan engines could be performed with lower error values. It is thought that the study helps in prediction of environmental effect regarding turbofan engines that are utilized at busy airports.

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Section: Solution Methodologymentioning

confidence: 95%

“…The least squares method is used to compute the coefficients in the MR method. In this way, it is aimed to minimize the difference between estimated values and actual values [ 35 ]. A classical multiple regression model is expressed in Eq.…”

Section: Solution Methodologymentioning

confidence: 99%

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Ucar

Aygün

Tanyeri

2023

J Therm Anal Calorim

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“…Arslan and Ertuğrul [15] analyzed the electricity price and compare the performance of multiple regression models and artificial neural networks models.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of The Travelling Time According to Weather Conditions Using Machine Learning Algorithms

CANBULUT

2023

Preprint

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Nowadays, a large part of the world’s population lives in urban areas. The increase in the population living in cities makes it difficult to live in cities.Local administrators seek solutions to these problems in order to make cities more livable. In order to increase the quality of transportation, which is among the important issues for local governments, studies are carried out to establish smart transportation systems for public transportation companies. Estimating travel times is an important tool in managing transportation operations. In addition, operations managers use these forecasts to coordinate future transportation operations. The public transport travel time prediction has been a well-researched topic; various researchers have predicted travel time using mathematical, statistical, and machine learning based models. Researchers also have compared the performance of these models. It can be seen that studies in this field can be divided into statistical methods and machine learning methods. And number of researchers use one of these methods. But in this study, we use both statistical and machine learning techniques together. Also, the parameters of this study are different from the other studies in this literature. The effect of the weather on the travel time will be analyzed. In this respect it is thought that the study will contribute to the literature. In the study, in addition to model tuning, sensitivity analyzes are also carried out according to the change of the k value in the KNN algorithm and the change of the threshold values used to determine outliers. we can say that, as can be seen from the analysis, the Catboost algorithm appears to be the algorithm that gives the best results in almost every situation.

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“…Bu sebeple çoğu ülkede ve Türkiye'de zaman içerisinde yeni reformlar yapılmış, elektrik piyasası; tüketicilerin ve özel kuruluşlarında katıldığı daha yenilikçi bir statü kazanmıştır. Böylece genel olarak enerji sektöründe büyüme başlamıştır (Arslan ve Ertuğrul, 2022;Çetintaş ve Bicil, 2015). Teknolojinin yoğun ve süratli şekilde ilerlemesi artan enerji talebine sebep olmuş ve enerji üzerine yapılan çalışmaları artırmıştır (Tanrıöven vd., 2011).…”

Section: Introductionunclassified

“…Teknolojinin yoğun ve süratli şekilde ilerlemesi artan enerji talebine sebep olmuş ve enerji üzerine yapılan çalışmaları artırmıştır (Tanrıöven vd., 2011). Enerji piyasasında ülkeler arasında enerji ticaretinin başlamasına sebep olan artan enerji ihtiyaçları ülkelerin enerji alt yapılarını güçlendirme çalışmaları için sebep oluşturmuştur (Arslan ve Ertuğrul, 2022). Enerji piyasasında 1980'li yıllarda Şili'de başlayan serbestleşme hareketinin zamanla diğer ülkelerde uygulanıp geliştirilmesiyle enerji fiyatlandırmasında yenilikçi bir yaklaşım başlamış ve bu model şeffaf olma özelliğinden dolayı başarıya ulaşmıştır.…”

Section: Introductionunclassified

A Study on Dynamic Energy Pricing in Smart Grids

Yalçinöz

Kaygusuz

2022

BBD

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Üreticiler ve tüketiciler arasında karşılıklı haberleşmenin sağlandığı akıllı şebekelerde (SG) talep tarafı yük yönetimi (DSLM), yük tahmini ve bunlarla bağlantılı olan dinamik enerji fiyatlandırması günümüzde ve gelecekte büyük önem arz etmektedir. Dinamik enerji fiyatlandırması ile elde edilen sinyallerin sunucular aracılığıyla yayınlanması talep tarafı yönetim ve enerji piyasası için çok önemli bir başlıktır. Dinamik enerji fiyatlandırması tabanlı çalışmaların genelinde dağıtık üretim koşullarına rağmen enerji dengesi talep tarafı yönetim ile korunmaktadır. Enerji fiyat sinyallerinin dinamik yapıda olmasıyla ve yük tahmini ile dağıtık üretimden kaynaklanan belirsizliklere yanıt verilmektedir. Bu çalışmada talep yanıtı (DR) yöntemleri ve akıllı fiyatlandırma planları olan kullanım süresi (ToU), kritik tepe fiyatlandırması (CPP) ve gerçek zamanlı fiyatlandırma (RTP) ayrıntılı olarak incelenmiştir. Akıllı şebekelerde yapılan çalışmalar, avantajları, dezavantajları incelenmiş ve akıllı fiyat planları karşılaştırılmıştır.

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Forecasting and Analysis in Turkish Electricity Market With Multiple Regression, Arima and Artificial Neural Network Methods

Cited by 6 publications

References 10 publications

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Optimized modeling of energy and environmental metrics of mixed flow turbofan engine used regional aircraft

Analysis of The Travelling Time According to Weather Conditions Using Machine Learning Algorithms

A Study on Dynamic Energy Pricing in Smart Grids

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