基于相似日与BiLSTM组合的短期电力负荷预测  

Short term power load forecasting based on the combination of similar days and BiLSTM

作　　者：祁宇轩 范俊岩 吴定会 汪晶 QI Yu-xuan;FAN Jun-yan;WU Ding-hui;WANG Jing(Key Laboratory of Advanced Process Control for Light Industry,Jiangnan University,Wuxi Jiangsu 214122,China;Shanghai Baosight Software Co.Ltd,Shanghai 201999,China)

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 [2]上海宝信软件有限公司,上海201999

出　　处：《控制理论与应用》2024年第12期2304-2314,共11页Control Theory & Applications

基　　金：国家重点研发项目(2020YFB1711100,2020YFB1711102)资助。

摘　　要：短期电力负荷存在非线性、波动性和影响因素多等特征,针对以上特征所导致的预测精度不足,本文提出一种基于相似日与双向长短时记忆神经网络(BiLSTM)组合的短期电力负荷预测模型.首先,剖析电力负荷的动态变化机理,基于相似日和灰色关联分析方法,构建负荷与特征融合数据集;其次,采用变分模态分解(VMD)方法将高波动、非线性的原始负荷数据分解为多个相对平稳的分量,并对各分量分别搭建BiLSTM预测模型;最后,采用鲸鱼算法(WOA)对模型的分解参数和相似日天数进行优化,减小模型的固有误差.以新英格兰某地区的实际数据进行仿真验证,所提模型的平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和均方根误差(RMSE)分别为0.58%,42,78,均优于对照模型,有效提升了负荷预测精度.Short term power load has the characteristics of nonlinearity,volatility and many influencing factors.Aiming at the lack of forecasting accuracy caused by the above characteristics,a short-term power load forecasting model based on the combination of similar days and bi directional long short memory neural network(BiLSTM)is proposed.First,the dynamic change mechanism of power load is analyzed,and the similar day and gray correlation analysis methods are introduced to build the load and feature fusion data set;Secondly,the nonlinear and highly fluctuating original load data is decomposed into several relatively stable components by using the variational modal decomposition(VMD)method,and the BiLSTM prediction model is built for each component;Finally,the whale optimization algorithm(WOA)is used to optimize the decomposition parameters and similar days of the model to reduce the inherent error of the model.Based on the actual data of a region in New England,the simulation results show that the MAPE,MAE and RMSE of the proposed model are 0.58%,42 and 78 respectively,which are better than the control model and effectively improve the accuracy of load forecasting.

关 键 词：短期电力负荷预测 相似日 深度学习 鲸鱼优化算法 变分模态分解 

分 类 号：TM715[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]