多维注意力机制改进燃气负荷预测  

Improvement of Gas Load Forecasting by Multi-dimensional Attention Mechanism

作　　者：曹辰光 徐晓钟[1] CAO Chen-Guang;XU Xiao-Zhong(College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 201400,China)

机构地区：[1]上海师范大学信息与机电工程学院,上海201400

出　　处：《计算机系统应用》2024年第1期185-191,共7页Computer Systems & Applications

基　　金：上海市科委项目(115105024)。

摘　　要：燃气负荷预测是一项让城市安全经济地调配燃气的重要的工作.目前,基于注意力机制的Seq2Seq模型越来越多地应用于燃气数据预测,是一种有效的燃气负荷预测方法.然而,针对燃气负荷数据这种突变频率高、幅度大的特点,一般基于注意力机制的Seq2Seq模型难以提取数据中的多维时间模式信息与应对数据随机突变情况,在处理影响因素复杂的燃气负荷的预测问题时仍然需要改进.为此,本文提出多维注意力机制Seq2Seq模型.一方面设计研究了多层次时间注意力模块,综合单时间步长、多时间步长的注意力计算提取数据中不同时间模式信息;另一方面,设计增加了局部历史注意力模块,以改进模型中无法区分重要历史信息的缺陷,使模型在预测时倾向于参考更为重要的历史信息.本改进模型针对燃气负荷的独特特性,具有较好的预测表现.使用国内某市区的燃气消耗数据与2016年电工数学建模竞赛的电力负荷数据的实验结果表明,本改进模型相对于一般注意力机制Seq2Seq模型,MAE分别降低了17%与9%.Gas load forecasting is an important task for cities to deploy gas safely and economically.At present,the Seq2Seq model based on the attention mechanism is increasingly utilized in gas data forecasting and is an effective method for gas load forecasting.However,the gas load data have such characteristics as high mutation frequency and large amplitude.The Seq2Seq model based on the general attention mechanism is difficult to extract the multivariate time pattern information in the data and deal with data random mutation.It is still necessary for improving gas load prediction with complex influencing factors.Therefore,this study proposes a multi-dimensional attention mechanism Seq2Seq model.On the one hand,a multi-level time attention module is designed and studied to integrate single-time step and multi-time step attention calculation to extract different time pattern information in the data.On the other hand,the design adds a local history attention module.By improving the model’s defect of distinguishing important historical information,the model tends to refer to more important historical information when making predictions.The improved model has better prediction performance for the unique gas load characteristics.The gas consumption data of an urban area in China and the electric load data of the 2016 electrical mathematical modeling competition are taken as examples.The experimental results show that the MAE of the improved model is reduced by 17% and 9% respectively compared with the general attention mechanism Seq2Seq model.

关 键 词：燃气负荷预测 注意力机制 Seq2Seq 

分 类 号：TU996.3[建筑科学—供热、供燃气、通风及空调工程]