Türki̇ye’deki̇ Demi̇ryolu Enerji̇ Tüketi̇mi̇ni̇n Yapay Si̇ni̇r Ağlari İle Tahmi̇n Edi̇lmesi̇

Abstract

A number of measures are being taken to protect the rapidly depleted energy resources around the world. The trend towards sustainable energy resources is increasing, especially in order to improve energy efficiency in transportation vehicles. In this study, the total energy consumption amounts of the railway vehicles were examined based on the line length, number of passengers and the amount of cargo in the last 43 years in our country. For 5 different models created by the artificial neural networks method, the amount of consumed energy and estimated energy amounts were compared using the correlation coefficients, R 2 , absolute error and absolute relative error criteria using Levenberg-Marquardt and Conjugate Gradient Descent algorithms. In the model 3, where the number of passengers and the amount of cargo were used as inputs, accuracy values and error criteria were better. According to the results obtained in the study, it was revealed that the amount of energy consumption is mostly related to the amount of load and then the number of passengers, and the change in line length and years is less effective. With the data obtained in this study, it will be possible to determine the amount of energy that can be spent by using the number of passengers planned to be transported on the railways in the future

Türkiye Kısa Dönem Elektrik Yük Talep Tahmininde Makine Öğrenmesi Yöntemlerinin Karşılaştırılması

“…Önerilen modellerin tahmin performansını değerlendirmek için literatürde sık kullanılan istatistiki değerlendirme ölçütlerinden Ortalama Hata Kare Kökü (RMSE), Ortalama Mutlak Hata (MAE) ve doğruluk kriterinden olan Belirleme Katsayısı 𝑅 2 kullanılmıştır [31][32][33][34][35]. RMSE, modeldeki tahmin hatalarının standart sapmasını hesaplamaktadır.…”

Makine Öğrenmesi Yöntemleri ile Konut Talep Tahmini

“…Training the network is the process of finding the optimum values of the weights [20]. In this study, each input is multiplied by the weight value (wij) that connects that input to the processing element and combined through the addition function given in Equation 1 [21]. Levenberg-Marquardt training algorithm was used because it is faster and more reliable than other training algorithms [22].…”

The Effect of Waste Ballast Aggregates on Mechanical and Durability Properties of Standard Concrete

“…As of 2020, there was a total of 12,803 km of national railway network in Turkey, of which there were 11,590 km of conventional lines and 1213 km of high-speed lines [ 48 ]. Considering Turkey in general, it is thought that approximately 5440 tons of waste ballast is produced annually.…”