摘要
随着新能源发电技术的快速发展与广泛普及,该类技术已经成为电力系统中关键的一环。其中,对新能源发电功率的准确预测对于电力系统的合理规划有着重要的意义。然而,现有的新能源发电功率预测方法仍存在以下挑战:1)基于深度神经网络的预测模型的超参数对模型的预测性能有着重要的影响,而目前大多数算法仍采用人工确定的方法为超参赋值;2)现有的预测模型难以高效地挖掘时序数据中的长期依赖关系,从而影响预测精度。针对上述问题,本文提出了一种基于CSO-LSTM(Competitive Swarm Optimizer-Long Short-Term Memory)的新能源发电功率预测方法,旨在利用一种两阶段的模型综合地提升预测性能。首先,在模型的第一阶段提出了一种基于竞争群优化的LSTM超参数优化算法,利用竞争群优化算法良好的探索能力和全局优化能力,实现预测模型超参数的自适应调整。然后,在模型的第二阶段设计了一种基于组合多门控机制的LSTM模型,该方法结合自注意力门控机制和组合多个门控网络用于挖掘新能源发电时序数据中的长期依赖关系,从而进一步地适应不同时间尺度下的新能源生成模式。最后,在2个真实数据集和1个仿真数据集上与4个先进的预测方法进行了对比实验,实验结果验证了提出的CSO-LSTM模型的有效性和执行效率。
With the rapid development and wide popularization of new energy generation technology,it has become a key part of the power system.Among them,the accurate prediction of new energy generation power is of great significance for the rational planning of power system.However,the existing new energy generation power prediction methods still have the following challenges:1)The hyperparameters of the prediction model based on deep neural network have an important impact on the prediction performance of the model,and most of the current algorithms still use the artificial method to assign the hyperparameters.2)It is difficult for the existing prediction models to efficiently mine the long-term dependencies in the time series data,thus affecting the prediction accuracy.To solve these problems,this paper proposes a CSO-LSTM(competitive swarm optimizer and long short-term memory)based method for the prediction of new energy generation power,which aims to use a two-stage model to comprehensively improve the prediction performance.Firstly,in the first stage of the model,a LSTM hyperparameter optimization algorithm based on competitive group optimization is proposed,which uses the good exploration ability and global optimization ability of competitive group optimization algorithm to realize the adaptive adjustment of the hyperparameters of the prediction model.Then,in the second stage of the model,a LSTM model based on the combined multi-gating mechanism is designed,which combines the self-attention gating mechanism and the combined multi-gating network to mine the long-term dependencies in the new energy generation time series data,so as to further adapt to the new energy generation patterns at different time scales.Finally,the proposed CSO-LSTM is compared with four advanced prediction methods on two real datasets and one simulation dataset,and the experimental results verify the effectiveness and efficiency of the proposed CSO-LSTM model.
作者
顾慧杰
方文崇
周志烽
朱文
马光
李映辰
GU Huijie;FANG Wenchong;ZHOU Zhifeng;ZHU Wen;MA Guang;LI Yingchen(China Southern Power Grid Company Limited,Guangzhou 510770,China)
出处
《计算机科学》
北大核心
2025年第S1期747-757,共11页
Computer Science
基金
中国南方电网有限责任公司科技项目(000005KC22120026)。
关键词
竞争群优化
长短期记忆神经网络
新能源发电功率预测
多尺度时序数据挖掘
参数优化
Competitive swarm optimization
Long-short term memory network
Power prediction of new energy generation
Multi-scale time series data mining
Parameter optimization
