基于遗传算法的风评估计算风非线性集成预测模型

Kaiping Lin, Xiaoyan Huang, Weiliang Liang, Binglian Chen
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引用次数: 2

摘要

根据数值天气预报中集合预测的思路,利用进化计算中的遗传算法(GA)建立了具有相同预期输出的多个神经网络,建立了广西大荣山风速计算的非线性人工智能集合预测(NAIEP)模型。结果表明,遗传神经网络非线性集成模型对风场的计算精度明显高于传统的多元线性回归模型。因此在实际应用中,通过人工神经网络非线性集成模型可以根据观测的短时间序列数据计算出风的长时间序列数据,因此该模型具有较好的实用性和推广价值,为研究风力资源的开发利用提供了依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Nonlinear Ensemble Prediction Model Based on Genetic Algorithm for Calculation Wind of the Wind Assessment
Following the thinking clue of the ensemble prediction in numerical weather prediction (NWP), a novel nonlinear artificial intelligence ensemble prediction (NAIEP) model for calculation wind speed of Mountain Darong in Guangxi has been developed based on the multiple neural networks with identical expected output created by using the genetic algorithm (GA) of evolutionary computation. The results show that the calculation accuracy by the nonlinear ensemble model of genetic - neural network for wind field is significantly higher than the traditional multiple linear regression model. Thus in practical application the long time sequence data of wind could be calculate according to the short time sequence data of the observation through the ANN nonlinear ensemble model, therefore this model is better practicability and popularize value for it provide the basis to research the exploitation wind resources.
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