Load prediction method and device, electronic equipment and storage medium

The invention discloses a load prediction method and device, electronic equipment and a storage medium. The method specifically comprises the steps of obtaining original load data of a target user; determining at least one influence feature of the original load data according to the original load da...

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Main Authors LUO XIAODONG, LIN CANQU, HUANG CHAOKAI, WU FUJIANG, LU WENXIAN, CAI LUXUAN, FENG YIFAN, HUANG WEIJIE, LIN ZHIJIAN, HUANG XIAOQI, LI NA, WANG ZHUOCHENG, LIN HONGHAO, DU NING, CHEN WEIXIANG, WANG SHUO, LIAO SHAOQIAN, HE JIAHAN, LU HAIMING, DU JIANGFAN, CHEN ZHENCHUAN, CHEN XIAOYU
Format Patent
LanguageChinese
English
Published 23.06.2023
Subjects
CALCULATING
CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRICITY
GENERATION
PHYSICS
SYSTEMS FOR STORING ELECTRIC ENERGY
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Summary:The invention discloses a load prediction method and device, electronic equipment and a storage medium. The method specifically comprises the steps of obtaining original load data of a target user; determining at least one influence feature of the original load data according to the original load data; and according to each influence feature, determining a prediction result of the electrical load of the target user. According to the technical scheme provided by the embodiment of the invention, the original load data is analyzed to obtain the features of different dimensions, and the electrical load of the target user is predicted according to the features. The characteristics of different dimensions have different influences on the electricity consumption of the target user, the characteristics are extracted from the original load sequence, the influence of the characteristics on the electricity consumption is determined through the machine learning model, the user load can be effectively predicted, and the a
Bibliography:Application Number: CN202310229294