Short-Term Power Load Forecasting Based on Improved SVR

doi:10.11930/j.issn.1004-9649.2016.08.054.05

Abstract

Abstract: In conventional SVR prediction algorithm, parameters including RBF kernel function parameters, insensitive coefficient and punish coefficient are selected manually. The random selected parameters lead to performance uncertainty. The initial parameters of artificial fish algorithm greatly influence optimization performance. By applying particle swarm optimization and chaotic theory to conventional artificial fish algorithm, the global optimization ability can be improved. Detailed analysis are performed are testing data of improved SVR load prediction model. Results show that the proposed method has better prediction accuracy and good engineering application value.

Key words: power short-term load forecasting, artificial fish optimization algorithm, particle swarm optimization, chaotic mechanism

CLC Number:

TM715

QIAN Zhi. Short-Term Power Load Forecasting Based on Improved SVR[J]. Electric Power, 2016, 49(8): 54-58.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.08.054.05

https://www.electricpower.com.cn/EN/Y2016/V49/I8/54

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