Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting ...Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.展开更多
不同海区的近岸海浪浪高具有明显差异性。针对当前大部分时间序列预测模型缺乏对不同地区(多源)浪高预测的适应性难题,提出了一种基于局部加权回归的多周期趋势分解(Seasonal and Trend decomposition using Loess,STL)和两级融合策略...不同海区的近岸海浪浪高具有明显差异性。针对当前大部分时间序列预测模型缺乏对不同地区(多源)浪高预测的适应性难题,提出了一种基于局部加权回归的多周期趋势分解(Seasonal and Trend decomposition using Loess,STL)和两级融合策略的浪高预测模型,简称为MSTL-WH(Multiple STL-Wave Height)。结合多源近岸浪高时间序列的多周期性、非线性和非平稳性的特点,首先利用周期图法提取多源近岸浪高数据集中的4个主要周期,并基于主要周期进行多次STL分解,将复杂的原始浪高序列分解为周期项、趋势项和余项;然后利用长短期记忆网络(Long Short Term Memory,LSTM)并结合两级融合策略,搭建近岸浪高预测网络;最后使用自注意力机制重新调整权重并输出未来12 h的浪高值。通过与当前主流时间序列预测方法对比,验证了所提方法在多源近岸浪高序列预测中具有较好的实用性和更低的预测误差。展开更多
“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取...“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取控制结果与风电场实际出力以及储能状态间的时序信息,通过构建基于双向长短时记忆循环神经网络的控制模型,使得风电场在多种运行工况下能够快速、准确地得到储能系统调节结果。基于实际风电场数据仿真结果表明,本文所提控制策略能够保证在一定经济效益的前提下,将风储系统控制误差保持在0.50%~1.37%。展开更多
基金the Gansu Province Soft Scientific Research Projects(No.2015GS06516)the Funds for Distinguished Young Scientists of Lanzhou University of Technology,China(No.J201304)。
文摘Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.
文摘不同海区的近岸海浪浪高具有明显差异性。针对当前大部分时间序列预测模型缺乏对不同地区(多源)浪高预测的适应性难题,提出了一种基于局部加权回归的多周期趋势分解(Seasonal and Trend decomposition using Loess,STL)和两级融合策略的浪高预测模型,简称为MSTL-WH(Multiple STL-Wave Height)。结合多源近岸浪高时间序列的多周期性、非线性和非平稳性的特点,首先利用周期图法提取多源近岸浪高数据集中的4个主要周期,并基于主要周期进行多次STL分解,将复杂的原始浪高序列分解为周期项、趋势项和余项;然后利用长短期记忆网络(Long Short Term Memory,LSTM)并结合两级融合策略,搭建近岸浪高预测网络;最后使用自注意力机制重新调整权重并输出未来12 h的浪高值。通过与当前主流时间序列预测方法对比,验证了所提方法在多源近岸浪高序列预测中具有较好的实用性和更低的预测误差。
文摘“双碳”背景下风电的渗透率不断提高,将对电力系统的形态和运行机制产生深刻影响。本文提出了一种基于双向长短期记忆Bi-LSTM(bidirectional long short-term memory)循环神经网络的风储系统控制策略。采用双向长短时循环神经网络提取控制结果与风电场实际出力以及储能状态间的时序信息,通过构建基于双向长短时记忆循环神经网络的控制模型,使得风电场在多种运行工况下能够快速、准确地得到储能系统调节结果。基于实际风电场数据仿真结果表明,本文所提控制策略能够保证在一定经济效益的前提下,将风储系统控制误差保持在0.50%~1.37%。
