Adıyaman ve Gaziantep İstasyonlarındaki Güneşlenme Şiddetinin Destek Vektör Makineleri ile Tahmini

DİNSEVER, Latif Doğan; Gümüş, Veysel; Şimşek, Oğuz; AVŞAROĞLU, Yavuz; KUŞ, Mehmet

doi:10.35193/bseufbd.904393

Cited by 1 publication

(3 citation statements)

References 39 publications

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“…In this research, the SVM algorithm, which is a non-parametric supervised classification method, was utilized. The SVM was originally designed for the classification of two-class linear data but was insufficiently developed for non-linear and multi-class data and applied to engineering applications [63,64].…”

Section: Support Vector Machinementioning

confidence: 99%

“…where ϕ(x) denotes the higher dimensional feature space transformed from the input vector x, and ω and b denote the weight vector and bias term, respectively. The risk function obtained by minimizing b and ω values is given in Equation ( 5) [64].…”

Section: Support Vector Machinementioning

confidence: 99%

“…The selection of an appropriate function is important in terms of obtaining more accurate results from the data to be used. In this study, the linear SVM function given in Equation (10) was determined [64].…”

Section: Support Vector Machinementioning

confidence: 99%

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Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

2023

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Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%