基于SF-Transformer的智能教育平台短期电力负荷预测研究  

Study on short-term electricity load forecasting based on SF-Transformer for intelligent education platform

作　　者：冯艳丽 周宇 黄福兴 万俊岭 袁培森[1] FENG Yanli;ZHOU Yu;HUANG Fuxing;WAN Junling;YUAN Peisen(College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210095,China;NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211106,China)

机构地区：[1]南京农业大学人工智能学院,南京210095 [2]南瑞集团有限公司(国网电力科学研究院有限公司),南京211106

出　　处：《华东师范大学学报（自然科学版）》2024年第5期173-182,共10页Journal of East China Normal University(Natural Science)

基　　金：国家自然科学基金(62377012);上海市大数据管理系统工程研究中心开放基金(HYSY21022)。

摘　　要：建设智能教育平台是推动教育智能化的一个重要过程,但智能教育平台依赖的人工智能模型在训练过程中会消耗大量电力,因此,开展短期电力负荷预测对建设智能教育平台具有重要意义.针对在考虑多个属性开展短期电力负荷预测时,由于部分属性与电力负荷数据的相关性不强并且Transformer无法捕捉电力负荷数据的时间相关性,而导致电力负荷预测不够准确的问题,基于SR(Székely and Rizzo)距离相关系数、融合时间定位编码和Transformer,提出了一种短期电力负荷预测模型SF-Transformer.SF-Transformer通过SR距离相关系数对影响电力负荷数据的属性进行筛选,选择与电力负荷数据之间SR距离相关系数较大的属性.SF-Transformer采用一种全局时间编码与局部位置编码相结合的融合时间定位编码,有助于模型全面获取电力负荷数据的时间定位信息.在数据集上开展了实验,实验结果表明SF-Transformer与其他模型相比,在两种时长上进行电力负荷预测具有更低的均方根误差和平均绝对误差.Building an intelligent education platform is an important process in the promotion of the intelligence of education.However,the artificial intelligence model on which intelligent education platforms rely consumes a large amount of electricity and energy during its training process,therefore,it is of great significance to carry out a short-term power load prediction for building an intelligent education platform.However,the major issue is the weak correlations between some attributes and power load data when considering multiple attributes during short-term power load forecasting,and the Transformer cannot capture the temporal correlation of power load data,which leads to a lack of accuracy in power load forecasting.Therefore,a short-term power load forecasting model,SF-Transformer is proposed that is based on the SR(Székely and Rizzo)distance correlation coefficient,fusion temporal localization coding and Transformer.The SF-Transformer filters the attributes that affect the power load data by using the SR distance correlation coefficient and selects the attributes that have higher SR distance correlation coefficients with the power load data.The SF-Transformer adopts fusion time localization coding,thereby combining global time coding and local position coding,which helps the model to comprehensively obtain time and localization information regarding power load data.Experiments conducted on the dataset show that SF-Transformer has a lower RMSE(root mean square error)and MAE(mean absolute error),compared with those of other power load forecasting models over two-time durations.

关 键 词：智能教育平台 短期电力负荷预测 SR距离相关系数 融合时间定位编码 TRANSFORMER 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]