Short-term generation forecasting for photovoltaic system based on CAPSO-RNN algorithm

doi:10.11930/j.issn.1004-9649.2012.4.87.4

Abstract

Abstract: Considering the characteristics of photovoltaic （PV） systems and various factors which will affect the power generation of PV systems, a short-term generation forecasting model of PV systems was proposed based on recurrent neural network （RNN） and chaos adaptive particle swarm optimization （CAPSO） algorithm. In the model, the weights and thresholds of RNN were optimized by the global optimization ability of CAPSO algorithm in order to avoid the disadvantages of the traditional RNN, such as slow convergence and prone to local minimum. The fuzzy membership function was used to process the data in the temperature evaluation, which can improve generation forecasting precision. Forecasting results show the high accuracy of the proposed model.

Key words: photovoltaic system, generation forecasting, chaos adaptive particle swarm optimization algorithm, recurrent neural network

CLC Number:

TM615

ZHAO Jie, ZHANG Yan-xia. Short-term generation forecasting for photovoltaic system based on CAPSO-RNN algorithm[J]. Electric Power, 2012, 45(4): 87-91.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2012.4.87.4

https://www.electricpower.com.cn/EN/Y2012/V45/I4/87

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