Urban electric vehicle charging demand prediction method and system based on intelligent agent simulation

• Main Authors: WEI LIYONG, LING SHUAI, HAO SHUANG, WANG CHENFEI, ZHANG JIAN, SONG JIE, ZHU XIANG, WANG XIAOYU, HUANG ZIJIAN, LI SHAOXIONG, LI SHUO, LI YUANYUAN, XIE QIN, HAN DANHUI
• Format: Patent
• Language: Chinese; English
• Published: 17.08.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; 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

The invention discloses an urban electric vehicle charging demand prediction method and system based on intelligent agent simulation, and belongs to the technical field of information in urban public traffic management. The method comprises the steps: S1, determination of traveler behavior parameters and forming of trajectory data: determining the traveler behavior parameters through a probability density function, wherein the probability density function is obtained by fitting existing data through Python, and real survey data of NHTS2017 is used as the track data; S2, electric vehicle charging selection determination: constructing a traveler charging selection model through a utility theory; and S3, a charging demand distribution prediction model: through a strategy of travel activity simulation priority, overcoming the defect that a traditional model determines the charging sequence of travelers under the condition of limited charging pile resources. According to the method, the limitation of the number of