Improved load adjustable capability clustering method

The invention relates to an improved load adjustable capability clustering method, which comprises the following steps of: 1, acquiring load data, establishing load power utilization behavior characteristic indexes, and performing primary clustering on loads with the same power utilization character...

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Bibliographische Detailangaben
Hauptverfasser:	ZHAO CHUNLI, SHANG XINGMING, WU YIXUAN, GAO ZIWEI, LI BEN, XU XICHEN, CHONG FEI, LEE CHANG-WON, WANG YUE, ZHANG HAO, LI DEQIANG, YE JU, LIU KAI, WU TONG, KONG LINGYU, DONG HANG, WEI GUOHUA, BAI JIE, GE LEIJIAO, LI BOZHEN, LIU ZHANGE, QIAN XIN
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING 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 relates to an improved load adjustable capability clustering method, which comprises the following steps of: 1, acquiring load data, establishing load power utilization behavior characteristic indexes, and performing primary clustering on loads with the same power utilization characteristics by using a k-means method; step 2, quantifying the adjustable capability of the loads with the same power utilization characteristics based on the primary clustering result of the loads with the same power utilization characteristics in the step 1, analyzing the uncertain confidence coefficient of the adjustable capability of the actual loads, and calculating the adjustable capability of the loads with the same power utilization characteristics based on the load data, the adjustable capability and the confidence level; and clustering the intra-class load adjustable capability by adopting a fuzzy c-mean algorithm. According to the invention, the accuracy and quality of load adjustable capability clustering ar