%0 Journal Article
%T 基于门控多层感知机和Informer的多通道电力负荷预测<br>Multi-Channel Power Load Forecasting Based on Gated Multilayer Perceptron and Informer
%A 孙卓远
%A 吕学文
%A 王继军
%J Artificial Intelligence and Robotics Research
%P 375-387
%@ 2326-3423
%D 2024
%I Hans Publishing
%R 10.12677/airr.2024.132039
%X 在电力领域，利用时间序列方法进行电力负荷预测已成为众多研究的热点。为了解决电力负荷预测准确率低的问题，本文提出了融合门控多层感知器和增强因果卷积的多通道时间序列融合网络GMEC-Informer，提高了模型捕捉长短期时间序列信息依赖的能力。为了证明本文模型的优越性，本文在广泛使用的数据集上与多个模型进行了比较，实验结果表明本文提出的GMEC-Informer具有更高的预测精度，可以为时间序列预测提供更好的研究方向。<br />
In the field of electric power, power load forecasting using time series methods has become a hot spot in many researches. In order to solve the problem of low accuracy of power load prediction, this paper adds gated multilayer perceptual units into the Informer model, and proposes GMEC-Informer, a multichannel time-series fusion network that fuses gated multilayer perceptron and enhanced causal convolution, which improves the model’s ability to capture long-short time series information dependence. In order to prove the superiority of the model in this paper, it is compared with several models on widely used datasets, and the experimental results show that GMEC-Informer has higher prediction accuracy and can provide a better research direction for time series prediction.
%K 负荷预测，因果卷积，深度学习，注意力机制<br>Power Forecasting
%K Causal Convolution
%K Deep Learning
%K Attention Mechanism
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=88029