Year 2020, Volume 7 , Issue 1, Pages 1 - 6 2020-04-11

Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction

Deogratias NURWAHA [1]


Four kernel functions of support vector machines (SVM), namely, radial basis function, sigmoid function, linear function and polynomial function, were applied for the prediction of solar cell output power. Two types of SVM model such as epsilon-SRV and nu-SVR were chosen for each kernel function. Measured values of temperature T (°C) and irradiance E (〖kWh.m〗^(-2)) were used as inputs and solar cell output power P (kW) was used as output. The accuracy of each kernel function was evaluated using well known statistical parameters. Radial basis function using nu-SVR and polynomial function using epsilon-SVR provided similar and better results than other kernels. However, polynomial function has taken more analysis run time while radial basis function used more number of support vectors than other kernels. They may be more computationally expensive.


Kernel function, PV output power, SVM
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Primary Language en
Subjects Engineering, Mechanical
Journal Section Research Article
Authors

Orcid: 0000-0002-5779-7909
Author: Deogratias NURWAHA (Primary Author)
Institution: University of Burundi
Country: Burundi


Dates

Application Date : October 8, 2019
Acceptance Date : February 27, 2020
Publication Date : April 11, 2020

Bibtex @research article { ijeat630789, journal = {International Journal of Energy Applications and Technologies}, issn = {}, eissn = {2548-060X}, address = {editor.ijeat@gmail.com}, publisher = {İlker ÖRS}, year = {2020}, volume = {7}, pages = {1 - 6}, doi = {10.31593/ijeat.630789}, title = {Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction}, key = {cite}, author = {NURWAHA, Deogratias} }
APA NURWAHA, D . (2020). Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction. International Journal of Energy Applications and Technologies , 7 (1) , 1-6 . DOI: 10.31593/ijeat.630789
MLA NURWAHA, D . "Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction". International Journal of Energy Applications and Technologies 7 (2020 ): 1-6 <https://dergipark.org.tr/en/pub/ijeat/issue/53708/630789>
Chicago NURWAHA, D . "Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction". International Journal of Energy Applications and Technologies 7 (2020 ): 1-6
RIS TY - JOUR T1 - Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction AU - Deogratias NURWAHA Y1 - 2020 PY - 2020 N1 - doi: 10.31593/ijeat.630789 DO - 10.31593/ijeat.630789 T2 - International Journal of Energy Applications and Technologies JF - Journal JO - JOR SP - 1 EP - 6 VL - 7 IS - 1 SN - -2548-060X M3 - doi: 10.31593/ijeat.630789 UR - https://doi.org/10.31593/ijeat.630789 Y2 - 2020 ER -
EndNote %0 International Journal of Energy Applications and Technologies Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction %A Deogratias NURWAHA %T Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction %D 2020 %J International Journal of Energy Applications and Technologies %P -2548-060X %V 7 %N 1 %R doi: 10.31593/ijeat.630789 %U 10.31593/ijeat.630789
ISNAD NURWAHA, Deogratias . "Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction". International Journal of Energy Applications and Technologies 7 / 1 (April 2020): 1-6 . https://doi.org/10.31593/ijeat.630789
AMA NURWAHA D . Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction. IJEAT. 2020; 7(1): 1-6.
Vancouver NURWAHA D . Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction. International Journal of Energy Applications and Technologies. 2020; 7(1): 6-1.