Multi-agents modelling of EV purchase willingness based on questionnaires

Journal of Modern Power Systems and Clean Energy

Juai Wu¹  Qiuwei Wu¹  Kang Li²  Dongliang Xie³  Yusheng Xue³  Fushuan Wen⁴  Guangya Yang⁵  Bin Cai⁶  Yu Zhang⁷ 

[1] Nanjing University of Science and Technology (NJUST),Nanjing,China,210094;Queen&x0027;State Grid Electric Power Research Institute (SGEPRI),Nanjing,China,210003;State Grid Shanghai Municipal Electric Power Company,Shanghai,China,200122;Technical University of Denmark,Lyngby,Denmark,2800;Zhejiang University,Hangzhou,China,310027;s University,Belfast,Northern Ireland,UK;

关键词: Behavioral analysis;    Experimental economics;    Human experimenters;    Knowledge extraction;    Multi- agents;    EV purchase;   

DOI  :  10.1007/s40565-015-0112-4

来源: DOAJ

【 摘 要 】

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant's decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers' willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.

【 授权许可】

Unknown