基于PAM-SSD-LSTM的短期风速预测

为提高短期风速预测的准确性，提出一种基于PAM聚类、奇异谱分解（SSD）和LSTM神经网络的组合预测模型来预测短期风速，以解决上述问题。首先，为提高神经网络的学习效率，采用PAM算法对原始风速数据进行相似日聚类；其次，SSD具有抑制模态混叠和虚假分量产生的优点，使用SSD分解风速序列，提取多尺度规律；最后，由于LSTM神经网络捕捉长时间依赖的序列的波动规律的能力较强，使用LSTM神经网络对分解后的风速分量进行预测，将各分量预测值叠加得到最终预测结果。实验结果表明，基于PAM-SSD-LSTM的组合预测模型可有效提高风速短期预测的准确率。

SHORT-TERM WIND SPEED PREDICTION BASED ON PAM-SSD-LSTM

Wind speed has characteristics of non-linearity,non-stationarity,intermittent and randomness. In order to improve the accuracy of short-term wind speed prediction,this paper proposes a combination prediction model based on PAM clustering, singular spectrum decomposition （SSD） and LSTM neural network to predict short-term wind speed to solve the problems mentioned above. Firstly, PAM algorithm is used to perform similar daily clustering on the original wind speed data to improve the learning efficiency of the neural network. Secondly,with the advantage of suppressing model aliasing and false components, SSD is used to decompose the wind speed series and extract multi-scale regular pattern. Finally, with a strong ability to capture the fluctuation law of long-term dependent series, LSTM neural network is used to predict the decomposed wind speed components. The prediction results of each component are accumulated to obtain the wind speed prediction results. Experiment results show that the combined prediction model based on PAM-SSD-LSTM can effectively improve the accuracy of short-term wind speed prediction.