An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization
An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization
复制标题
使用 GA 和 PSO 优化进行风速预测的两种小波-MLP 混合框架的实验研究
DOI：
10.1016/j.ijepes.2013.03.034
复制
发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Yan-fei Li
中科院分区：
文献类型：
--
作者：
Hui Liu;Hong-qi Tian;Chao Chen;Yan-fei Li
风速序列表现出非定常和非线性现象。风速的准确预报对可再生能源的安全利用具有重要意义。与使用单一算法的预测模型相比，混合模型总是具有更高的精度。基于小波理论、经典时间序列分析、遗传算法、粒子群优化和人工神经网络，提出了小波-遗传算法(GA)-多层感知器（MLP）和小波-粒子群优化(PSO)-多层感知器（MLP）两种混合预测框架来预测非平稳风速。比较了不同算法组合的预测性能，探讨了两种混合框架中不同成分的贡献。基于3个试验实例的结果表明：(1)两种混合预测框架均能满足风速预测的不同精度要求，可应用于风力发电系统；(2)在两种混合框架中，GA和PSO分量对改进MLP的贡献均不显著，而小波分量对改进MLP的贡献均显著。
Wind speed series show unsteady and nonlinear phenomena. The accurate forecast of wind speed is important for the safety of renewable energy utilization. Compared to the prediction models which use single algorithms, hybrid models always have higher accuracy. Based on the theories of Wavelet, classical time series analysis, genetic algorithm, particle swarm optimization and artificial neural networks, two hybrid forecasting frameworks [the Wavelet-Genetic Algorithm (GA)-Multilayer Perceptron (MLP) and the Wavelet-Particle Swarm Optimization (PSO)-Multilayer Perceptron (MLP)] are proposed to predict non-stationary wind speeds. Comparisons of forecasting performance using different algorithm combinations are provided to investigate the contribution of different components in those two hybrid frameworks. The results based on three experimental cases show that: (1) both of the two proposed hybrid forecasting frameworks are suitable for the diverse accuracy requirements in wind speed predictions, which can be applied to wind power systems; and (2) in both of the two hybrid frameworks, the contribution of the GA and the PSO components in improving the MLP are not statistically significant while that of the Wavelet component is statistically significant.
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DOI：
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影响因子：
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