摘要
为提高负荷预测精度,提出一种基于混沌定向布谷鸟算法优化Elman-IOC神经网络的短期负荷预测模型,首先对Elman神经网络拓扑结构进行改进设计,通过增添输入-输出层连接单元,加强网络并行运算能力,提高预测精度,然后在布谷鸟算法中,利用最优位置信息指导随机游动过程,同时引入混沌扰动算子,增强全局搜索能力,最后将算法应用于Elman-IOC神经网络参数优化,建立了短期负荷预测模型。实验结果表明,较之其他模型,此模型具有更高的预测精度。
In order to improve the accuracy of load forecasting, a short-term load forecasting model based on Elman-IOC neural network with chaotic oriented cuckoo optimization algorithm was proposed in this paper. Firstly, the Elman neural network topology is improved by adding the input-output layer connection unit, the network parallel computing capability is enhanced and the prediction accuracy is improved. Then, in the cuckoo algorithm, the optimal location information is used to guide the random walk process. Meanwhile, the chaos disturbance operator is introduced to enhance the global search ability. Finally, the algorithm is applied to Elman-IOC neural network parameter optimization, and a short-term load forecasting model is established. The experimental results show that compared with other models, this model has higher prediction accuracy.
作者
杨芳君
王耀力
王力波
常青
Yang Fangjun;Wang Yaoli;Wang Libo;Chang Qing(School of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, Shanxi,China)
出处
《电测与仪表》
北大核心
2019年第9期32-37,共6页
Electrical Measurement & Instrumentation
基金
全国工程专业学位研究生教育指导委员会立项项目(2016-ZX-095)
山西省自然科学基金(201801D121141)
