SHORT TERM WIND POWER PREDICTION BASED ON EN-SKPCA DIMENSIONALITY REDUCTION AND FPA OPTIMIZING LSTMNN

Zhang Shuqing, Yang Zhenning, Jiang Anqi, Li Jun, Liu Haitao, Mu Yong

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (6) : 204-211.

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (6) : 204-211. DOI: 10.19912/j.0254-0096.tynxb.2020-1025

SHORT TERM WIND POWER PREDICTION BASED ON EN-SKPCA DIMENSIONALITY REDUCTION AND FPA OPTIMIZING LSTMNN

  • Zhang Shuqing1, Yang Zhenning1, Jiang Anqi1, Li Jun1, Liu Haitao1, Mu Yong2
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Abstract

Comprehensively considering the characteristics of wind power series and the multi-dimensional meteorological data, a dimensionality reduction method of elastic net improved kernel principal component analysis (EN-SKPCA) is proposed. The dimensionality of meteorological factors is reduced and expressed as a regression optimization problem. The added elastic network penalty solve the problem that the KPCA reconstruction principal component is difficult to explain. The flower pollination algorithm (FPA) is proposed to optimize the long-short-term memory neural network (LSTMNN) prediction. The model can automatically select the best super parameters and reduce the randomness caused by the empirical setting of parameters. The method solves the influence of abrupt weather and improves the prediction accuracy. The superiority of this method is proved by the experiment on the measured data of Mahuangshan No.1 wind farm in Ningxia in 2017.

Key words

wind power / power predication / meteorology / dimensionality reduction / elastic net sparse kernel principal component analysis / flower pollination algorithm optimizing / long short-term memory neural network

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Zhang Shuqing, Yang Zhenning, Jiang Anqi, Li Jun, Liu Haitao, Mu Yong. SHORT TERM WIND POWER PREDICTION BASED ON EN-SKPCA DIMENSIONALITY REDUCTION AND FPA OPTIMIZING LSTMNN[J]. Acta Energiae Solaris Sinica. 2022, 43(6): 204-211 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1025

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