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Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed

机译：基于奇异频谱分析的两种组合预测模型及短期风速智能优化算法

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摘要

Short-term wind speed forecasting has become an important technology to utilize sustainable energy, reduce the impact of wind power grid and improve the control of wind turbines. Current forecasting models based on individual algorithm and hybrid optimization algorithm could be applied to a variety of wind speed forecast. However, these algorithms ignored the limitation of optimizing parameter and the use of recent data, which may result in forecasting poor accuracy. In this paper, integrated approaches, combining singular spectrum analysis technique, cuckoo search algorithm (CSA) and harmony search algorithm (HSA), and back propagation neural network (BPNN), were introduced for conducting short-term wind speed forecasting. Firstly, the SSA technique is used for identifying and extracting instable components from raw wind speed signals. Then, the parameters of BPNN are employed to be optimized by the CSA/HSA, which improve precision of forecast results. Finally, the BPNN is utilized to deal with the wind speed series. The proposed hybrid model is conducted by the wind speed at three stations located in Penglai, China. Our experiment reveals that the proposed hybrid model can generate a more accurate, reliable and robust result and SSA technique is found to be superior technique to preprocess wind speed series. Furthermore, we compare to the forecast results with SSA and non SSA denoising procedure and found that hybrid model with SSA technique outperforms other prediction model.
机译：短期风速预测已成为利用可持续能源的重要技术，降低风电网的影响，提高风力涡轮机的控制。基于单个算法和混合优化算法的电流预测模型可以应用于各种风速预测。然而，这些算法忽略了优化参数和最近数据的使用的限制，这可能导致预测差的准确性差。本文介绍了集成方法，结合奇异谱分析技术，Cuckoo搜索算法（CSA）和和声搜索算法（HSA）及反向传播神经网络（BPNN），用于进行短期风速预测。首先，SSA技术用于从原始风速信号识别和提取不动的组件。然后，采用BPNN的参数由CSA / HSA进行优化，从而提高预测结果的精度。最后，使用BPNN来处理风速系列。拟议的混合模型是由位于中国蓬莱的三站的风速进行。我们的实验揭示了所提出的混合模型可以产生更准确，可靠且稳健的结果，并且发现SSA技术是预处理风速系列的优越技术。此外，我们与SSA和非SSA去噪程序的预测结果进行比较，发现具有SSA技术的混合模型优于其他预测模型。

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