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首页> 外文期刊>Renewable energy >Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation
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Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

机译：基于长短短期记忆，云模型和非参数核密度估计的风电的短期预测与不确定度分析

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Conventional wind power forecasting (WPF) methods adopt deterministic forecasting methods to produce a definite value of wind power output at a future time instant. However, any forecasting involves inherent uncertainty, and the uncertainty in WPF cannot be described by deterministic forecasting methods. Because WPF has the properties of time series data and long short-term memory (LSTM) is a time recursive neural network, the latter has significant advantages in forecasting the time series events. Therefore, in this study, a short-term WPF method based on the improved LSTM model is proposed, and the output power of a wind farm is calculated. The results show that the 4-h, 24-h, and 72-h forecasting accuracies of LSTM are higher than those of the back propagation (BP) neural network, the Particle swarm optimization and back propagation neural network (PSO-BP) hybrid model, and the wavelet neural network (WNN) at different time scales and seasons. The uncertainties in WPF performed by different forecasting models at different time scales are qualitatively described by the expectation, entropy, and hyper-entropy of cloud model. The uncertainties in WPF are quantitatively calculated by the confidence intervals based on the non-parametric kernel density estimation (NPKDE). The calculated results show that the proposed method can accurately predict the uncertainties in WPF at different confidence levels. The optimal operation results of reserve capacity based on the uncertainty in WPF and the optimal operation of the distribution network containing wind power and electric vehicles show that the proposed method can further improve the economic benefits of wind farm and distribution network. (c) 2020 Elsevier Ltd. All rights reserved.
机译：传统的风力预测（WPF）方法采用确定性预测方法，以在未来的时间瞬间产生风电输出的明确值。然而，任何预测都涉及固有的不确定性，并且无法通过确定性预测方法描述WPF中的不确定性。由于WPF具有时间序列数据的属性和长期内存（LSTM）是一个时间递归神经网络，所以后者在预测时间序列事件方面具有显着的优势。因此，在本研究中，提出了一种基于改进的LSTM模型的短期WPF方法，并计算风电场的输出功率。结果表明，LSTM的4-H，24-H和72-H预测精度高于后传播（BP）神经网络，粒子群优化和反向传播神经网络（PSO-BP）混合的高精度在不同时间尺度和季节的模型和小波神经网络（Wnn）。由不同时间尺度的不同预测模型执行的WPF中的不确定性由云模型的期望，熵和超熵进行定性描述。 WPF中的不确定性由基于非参数核密度估计（NPKDE）的置信区间隔定量计算。计算结果表明，该方法可以在不同置信水平上准确地预测WPF中的不确定性。基于WPF不确定性的储备容量的最佳运行结果和包含风电和电动汽车的配电网络的最佳运行表明，该方法可以进一步提高风电场和分销网络的经济效益。 （c）2020 elestvier有限公司保留所有权利。

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