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首页> 外文期刊>Renewable Power Generation, IET >Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series
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Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series

机译：基于混合预测模型的数据挖掘和遗传算法自适应粒子群优化：以风速时间序列为例

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摘要

Wind energy has been part of the fastest growing renewable energy sources and is clean and pollution-free. Wind energy has been gaining increasing global attention, and wind speed forecasting plays a vital role in the wind energy field. However, such forecasting has been demonstrated to be a challenging task due to the effect of various meteorological factors. This study proposes a hybrid forecasting model that can effectively provide preprocessing for the original data and improve forecasting accuracy. The developed model applies a genetic algorithm-adaptive particle swarm optimisation algorithm to optimise the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined in regard to the wind farms of eastern China. The forecasting performance demonstrates that the developed model is better than some traditional models (for example, back propagation, WNN, fuzzy neural network, and support vector machine), and its applicability is further verified by the paired-sample tests.
机译：风能一直是增长最快的可再生能源的一部分，并且清洁，无污染。风能已经越来越受到全球的关注，风速预测在风能领域中起着至关重要的作用。但是，由于各种气象因素的影响，这种预报已被证明是一项艰巨的任务。这项研究提出了一种混合预测模型，该模型可以有效地为原始数据提供预处理并提高预测准确性。所开发的模型应用遗传算法自适应粒子群优化算法来优化小波神经网络（WNN）模型的参数。随后针对中国东部的风电场对提出的混合方法进行了研究。预测性能表明，所开发的模型优于某些传统模型（例如，反向传播，WNN，模糊神经网络和支持向量机），并且通过配对样本测试进一步验证了其适用性。

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