Self-paced learning long short-term memory based on intelligent optimization for robust wind power prediction

IF 2.2 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Shun Yang, Xiaofei Deng, Dongran Song
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引用次数: 0

Abstract

Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi-objective sand cat swarm algorithm (MO-SCSO) and a self-paced long short-term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self-paced learning is used to effectively solve the instability caused by noisy data during long short-term memory network (LSTM) training. Following this, the improved MO-SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO-SCSO-spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.

基于智能优化的自学式长短期记忆,用于稳健的风能预测
鉴于风能的不可预测性和间歇性,风力发电的精确预测对于确保电力系统的安全稳定运行至关重要。为了降低噪声数据对风电预测鲁棒性的影响,本文提出了一种利用增强型多目标沙猫群算法(MO-SCSO)和自步长短时记忆网络(spLSTM)的风电预测方法。首先,将实际风力数据处理成时间序列作为输入和输出。然后,利用自步进学习的渐进优势,有效解决了长短期记忆网络(LSTM)训练过程中由噪声数据引起的不稳定性。之后,改进的 MO-SCSO 被用来迭代优化 spLSTM 的超参数。最终,构建了一个用于风力预测的 MO-SCSO-spLSTM 组合模型。该模型通过奥地利陆上风电场和丹麦海上风电场的数据进行了验证。实验结果表明,与传统的 LSTM 预测方法相比,所提出的方法具有更好的预测精度和鲁棒性。具体而言,在陆上和海上风电预测实验中,所提方法的最小 MAE 分别降低了 5.44% 和 4.96%,MAE 范围分别缩小了 4.45% 和 17.21%,有利于电力系统的安全稳定运行。
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来源期刊
IET Control Theory and Applications
IET Control Theory and Applications 工程技术-工程:电子与电气
CiteScore
5.70
自引率
7.70%
发文量
167
审稿时长
5.1 months
期刊介绍: IET Control Theory & Applications is devoted to control systems in the broadest sense, covering new theoretical results and the applications of new and established control methods. Among the topics of interest are system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation. The scope encompasses technological, economic, physiological (biomedical) and other systems, including man-machine interfaces. Most of the papers published deal with original work from industrial and government laboratories and universities, but subject reviews and tutorial expositions of current methods are welcomed. Correspondence discussing published papers is also welcomed. Applications papers need not necessarily involve new theory. Papers which describe new realisations of established methods, or control techniques applied in a novel situation, or practical studies which compare various designs, would be of interest. Of particular value are theoretical papers which discuss the applicability of new work or applications which engender new theoretical applications.
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