摘要
新一代电网是未来智能电网发展的主要方向,而电力负荷精准预测是智能电网的一项重要基础工作.为了提高智能电力系统负荷预测的精确度,本文基于自相关机制的预测模型Autoformer,对负荷数据集进行了特性分析,在原模型中加入特征提取层,从编码层数、解码层数、学习率和批量大小等方面优化了模型参数,实现了周期灵活的负荷预测.在真实数据集上进行了实验和分析,实验结果表明,本文模型在预测效果上表现更好,MAE(mean absolute error)和MSE(mean squared error)分别为0.2512和0.1915,决定系数为0.9832.与其他方法相比,本文方法负荷预测效果更好.
Next-generation power grids is the main direction of future smart grid development,and the accurate prediction of power loads is an important basic task of smart grids.To improve the accuracy of load prediction in smart power systems,this work characterized the load dataset based on an Autoformer,a prediction model with an autocorrelation mechanism;adds a feature extraction layer to the original model;optimized the model parameters in terms of the number of coding layers,decoding layers,learning rate,and batch size;and achieved cycle-flexible load prediction.The experimental results show that the model performs better in prediction,with an MAE,MSE,and coefficient of determination of 0.2512,0.1915,and 0.9832,respectively.Compared with other methods,this method has better load prediction results.
作者
唐利涛
张智勇
陈俊
许林娜
钟佳晨
袁培森
TANG Litao;ZHANG Zhiyong;CHEN Jun;XU Linna;ZHONG Jiachen;YUAN Peisen(Measurement Center of Guangxi Power Grid Co.Ltd.,Nanning 530024,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第5期135-146,共12页
Journal of East China Normal University(Natural Science)
基金
国家自然科学基金(61877018)
上海市大数据管理系统工程研究中心开放基金(HYSY21022)。
