微型电脑应用2025，Vol.41Issue(9)：88-92,5.
基于机器学习的动态时间窗地理空间负荷预测方法
A Machine Learning-based Dynamic Time Window Geospatial Load Forecasting Method
摘要
Abstract
Spatial load forecasting(SLF)can accurately predict the time period,spatial location,and scale of future loads.Geo-graphic information system(GIS)is a technology that combines the advantages of computer technology,databases,and infor-mation systems,capable of storing and processing geographic locations and their related attribute information.This paper starts from the GIS system,integrates load data,and uses generative adversarial networks(GAN)to expand the dimensionality of the load dataset,achieving dynamic time window geospatial load forecasting.The paper employs this method in conjunction with actual load data from a certain area for case analysis and verification.关键词
机器学习/空间负荷预测/地理信息系统/生成对抗网络/动态负荷预测Key words
machine learning/spatial load forecasting/geographic information system/generative adversarial network/dynamic load forecasting分类
动力与电气工程引用本文复制引用
夏爱民,傅彬,国宗,顾超越,吴程楠,李家睿..基于机器学习的动态时间窗地理空间负荷预测方法[J].微型电脑应用,2025,41(9):88-92,5.基金项目
国家重点研发计划项目(2020YFB2104500) （2020YFB2104500）
