基于Agent的参数化Bass网络扩散模型预测PEV的采用
Prediction of PEV Adoption with Agent-Based Parameterized Bass Network Diffusion Model
摘要：
尽管电动汽车（EV）数量的增长正在引领我们进入一个更加可持续的世界，但它也为制造商的生产规划、充电设施提供商的扩张计划以及能源系统适应更大电力需求带来了挑战。为了应对这些挑战，预测电动汽车在地理范围内增长的模型将有所帮助。在这项研究中，基于代理的参数化低音网络扩散模型的电动汽车在华盛顿州的人口数据。该模型包括收入水平和邻居数量作为决定EV扩散概率的两个关键因素。通过模拟得到的参数估计，模型在时间和地理范围内对华盛顿州电动汽车的采用情况都有很高的估计精度。该模型可用于预测华盛顿州电动汽车的增长，并可用于其他地理区域。
英文摘要：
Although the growing electric vehicle (EV) population is leading us into a more sustainable world, it is also bringing challenges for the manufacturers's production planning, the charging facility providers's expansion plan, and the energy system's adaption to greater electricity demand. To tackle these challenges, a model to predict EV growth in geographical scope would be helpful. In this study, an agent-based parameterized bass network diffusion model was developed for EV population data in Washington. The model included income levels and number of neighbors adopted as two key factors in determining EV diffusion probabilities. With the parameters estimated from simulation, the resulting model achieve a high estimation accuracy for EV adoption in Washington in both temporal and geographical scopes. This model could be used to predict EV growth in Washington, and to be adopted to other geographical areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
