Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

Bo Gu, Tianren Zhang, Hang Meng and Jinhua Zhang

Renewable Energy, 2021, vol. 164, issue C, 687-708

Abstract: 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.

Keywords: Long short-term memory (LSTM); Cloud model (CM); Non-parametric kernel density estimation (NPKDE); Wind power forecasting (WPF); Short-term forecasting; Uncertainty analysis (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:164:y:2021:i:c:p:687-708

DOI: 10.1016/j.renene.2020.09.087

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