Source load power prediction method and device considering different granularities, and storage medium

The invention discloses a source load power prediction method and device considering different granularities, and a storage medium. The method comprises the steps of collecting source load historical data in a to-be-predicted region; preprocessing the source load historical data to obtain a pluralit...

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Hauptverfasser:	ZHU NANYANG, SHAN XIN, KANG HYUK, WANG YI, SONG XIAOXIAO, XU HAO, CAO GUOFANG, HE XIN, PENG LONG, ZHANG KAIFENG, MA SHOUDA, LUO YUCHUN, ZHANG YUANJUE, FU JIAYU, GUO FAN, DAI ZEMEI, ZHANG LEI, HU DIANGANG, LU JUANJUAN, QIU JINZHE, ZHOU YI, XU HUAQI, QIU ZIHANG, LI GEN, WANG JIAN, ZHOU LIANGCAI, YANG JIE
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	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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Beschreibung
Zusammenfassung:	The invention discloses a source load power prediction method and device considering different granularities, and a storage medium. The method comprises the steps of collecting source load historical data in a to-be-predicted region; preprocessing the source load historical data to obtain a plurality of groups of data groups with different granularities; respectively inputting the plurality of groups of preprocessed data groups with different granularities into a pre-constructed and trained MultiGNet prediction model to obtain a prediction result of the power of the two sides of the source load; wherein the MultiGNet prediction model is used for extracting semantic dependency features of data groups with different granularities through a cross-granularity learning module, establishing a relationship among the data groups with different granularities, and analyzing a feature relationship among the data groups with different granularities through a granularity attention module so as to obtain optimal weights of