The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies ...
电压数据来自现实世界,因此存在许多噪点,利用离散小波转换(DWT)对电压数据进行降噪,使正常电压数据归于平稳,局部放电现象更易被察觉。 项目结果 利用Lstm很好的对未来电压值进行了预测,预测准确率达到85.3%。 但是,即使对于Lstm,序列的长度仍然太长了(200-300更佳),若能对数据序列进行压缩,有可能得到更好的预测结果。
As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies
摘要 为提高流感预测模型的准确率,针对流感数据的季节性与波动性特点,提出利用离散小波分解(DWT)、季节性自回归综合移动平均模型(SARIMA)和长短期记忆神经网络(LSTM)综合建模,构建DWT-SARIMA-LSTM混合预...展开更多 In order to improve the accuracy of influenza forecasting models,considering the seasonality and ...
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基于注意力机制的DWTLSTM电力负荷预测方法,采用离散小波分解方法对采集到的原始负荷数据进行分解,得到不同尺度负荷分量;引入注意力机制,根据各尺度负荷分量的重要程度进行自适应赋权,完成负荷数据的预处理;利用改进的PSO粒子群算法对LSTM长短期记忆神经网络模型参数进行寻优,得到优化后的LSTM模型;将赋权后的各负荷分量分别...
Our results demonstrate that the DWT-LSTM method achieves the highest accuracy, with MAPE ranging from 0.59% to 4.2% for Iran and 0.29% to 3.02% for Germany, across hour-ahead to year-ahead forecasts. Moreover, during special events and festivals, DWT-LSTM exhibits precise forecasting ...
These results indicate that the DWTLSTM outperforms conventional methods such as k-nearest neighbor (k-NN), linear discriminant analysis (LDA), support vector machine/support vector regression (SVM/SVR), multilayer perceptron (MLP), and even standard long-short term memory (LSTM)....
本发明公开了一种基于DWTLSTM神经网络的可调节负荷预测方法,属于可调节负荷预测技术领域,包括以下步骤:采集可调节负荷历史数据;通过离散小波变换DWT依次对可调节负荷历史数据进行三次变换,得到变换数据;将变换数据进行重构,得到三个重构数据;将三个重构数据进行权重调节,并将权重调节后的三个重构数据进行叠加,将叠加后的...