Abstract
EMS (Energy management system) is a collection of computer hardware and software, which collects, monitors, controls and optimizes data provided by power control system, and provide trading scheme, security services and service analysis for power market. The prediction of status data is a basic function module of advanced application software systems. Therefore it is meaningful to do research on new method and new technology of predicting power grid status data. In this paper, support vector machine is used to do regression prediction for active power of EMS. In training process, the training set and kernel function of SVM are selected, and parameters are optimized, also, the performance of SVM is evaluated. Experiments show that SVM can get higher accuracy in short term active power prediction although the data set is small. This paper provides a new idea for related research works in electric power industry system.
Funded by the national high technology research and development program (863 Program) (No. 2015AA050204)
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Su, J., Yang, Y., Yan, D., Tang, Y., Mu, Z. (2017). Research on Short-Term Prediction of Power Grid Status Data Based on SVM. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_21
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