Method for predicting regional saturation capacity based on nonparametric model

• Main Authors: NIU XINSHENG, LU KUAN, XU NAN, ZHAO XIN, TIAN XIN, CAO XIANGYANG, ZHU YI, LI BO, PENG HONGQIAO, WU KUIHUA, ZHANG TIANBAO, LIANG RONG, WANG YUAN, XUE WANLEI, WANG CHUNYI, GU JIE, MOU HONG
• Format: Patent
• Language: Chinese; English
• Published: 07.07.2017
• Subjects: 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

The invention discloses a method for predicting a regional saturation capacity based on a nonparametric model. The method comprises the steps of (1) establishing a nonparametric regression model, introducing a Gaussian kernel weight function, using a local polynomial estimation method to carry out estimation, and determining a mapping relation between the power demand and influence factors, (2) establishing a nonparametric cumulative model, introducing a secondary planning problem, and confirming a cumulative coefficient based on the nonparametric regression model, (3) selecting an impact factor, (4) selecting an order and a bandwidth according to the amount of collected data, and (5) combining data and substituting the data into the nonparametric regression model and the nonparametric cumulative model to predict electricity consumption and saturation power. According to the established nonparametric cumulative model, the precision of regional saturation capacity prediction is greatly improved, the computatio