Method and system for checking monthly electricity quantity and electricity charge abnormity of residential users, and storage medium

Method and system for checking monthly electricity quantity and electricity charge abnormity of residential users, and storage medium

The invention discloses a method and system for checking monthly electricity quantity and electricity charge abnormity of residential users, and a storage medium. The method comprises the following steps: acquiring monthly average human body comfort of residential users, monthly holiday days of a to...

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Bibliographische Detailangaben
Hauptverfasser: HUANG GONGYUE, ZHUANG WANLING, CHEN MIN, XUE BING, LIN SIYUAN, SUN MENGLONG, CHEN HUAFENG, LIN BINGHONG, PAN LIU, LIU JIANZHU, WANG HAITAO, CHEN LIN, HUANG ANZI, FU TINGTING, HUANG MEI, CHEN HUI, SHI LONGYAN, LI YIJUN, LI YAN, DONG PEICHUN, LIU JIAXUE, LIN LEI
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:The invention discloses a method and system for checking monthly electricity quantity and electricity charge abnormity of residential users, and a storage medium. The method comprises the following steps: acquiring monthly average human body comfort of residential users, monthly holiday days of a to-be-predicted month, workday days of the to-be-predicted month, holiday days of the to-be-predicted month, monthly electricity consumption information of all months before the to-be-predicted month, and residential user type information; calculating he monthly average human body comfort of the resident user, the monthly holiday number of the to-be-predicted month, the workday number of the to-be-predicted month, the holiday number of the to-be-predicted month and the electricity consumption information of all months before the to-be-predicted month; inputting the resident type information into a pre-trained XGBoost model for monthly electricity consumption prediction of the to-be-predicted month to obtain a predict