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Prediction of Market Power Using SVM as Regressor Under Deregulated Electricity Market
Shafeeque Ahmed K, Fathima F, Ananthavijayan R, , Sahoo S.K, Naidu R.C.
Published in Springer Singapore
Volume: 437
Pages: 605 - 617
This paper proposes a methodology to utilize support vector machines (SVM) as a regressor tool for predicting market power. Both the companies, i.e., Generation (Gencos) and the Distribution (Discos), can utilize this tool to forecast market power on their perspective. Attributes and criterion are to be chosen properly to classify market power. In this paper, the effectiveness of SVM technique in predicting market power is formulated. Independent system operator (ISO) can also use this tool as regressor and it is discussed elaborately. Both linear and nonlinear kernels are compared. Nodal must run share (NMRS) is used as an index for predicting market power. A sample of three-bus system consisting of two generators and one load/two loads is used to illustrate the study. © Springer Science+Business Media Singapore 2016.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of Fifth International Conference on Soft Computing for Problem Solving
PublisherData powered by TypesetSpringer Singapore
Open Access0