电动汽车充电站的电力消耗预测和预测收入
K C Akshay1, G Hannah Grace2, Kanimozhi Gunasekaran3
1School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Scientific reports
|March 19, 2024
概括
季节性自动回归集成移动平均线 (SARIMA) 模型准确预测电动汽车充电站的电力需求和收入. 这有助于优化运营和规划基础设施,支持向可持续运输过渡.
科学领域:
- 电气工程 电气工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 电动汽车 (EV) 提供零排放,解决传统汽车的环境和健康问题.
- 越来越多的电动汽车采用需要有效管理充电站的电力消耗.
- 准确的预测对于优化运营,防止电网问题和规划基础设施至关重要.
研究的目的:
- 开发一个强大的时间序列模型来预测电动汽车充电站的功率使用.
- 评估不同时间序列模型在预测电力需求和收入方面的准确性.
- 分析影响功耗的因素,如车辆类型和充电方法.
主要方法:
- 使用了三种时间序列模型:自动回归移动平均 (ARMA),自动回归集成移动平均 (ARIMA) 和季节性自动回归集成移动平均 (SARIMA).
- 用科罗拉多州公共收费点的四年数据和印度喀拉拉邦的六个月数据验证模型.
- 将车辆类型 (两,三,四轮车) 和充电方法 (交流与直流) 纳入电力使用预测中.
主要成果:
- 萨里马模型表现出卓越的性能,实现了最低的根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE).
- 四轮车比两轮和三轮车消耗更多的电力.
- 直流 (DC) 充电设施比交流 (AC) 充电站使用更多的电力.
结论:
- 萨里马是准确预测电动汽车充电站功率和收入的重要工具.
- 这些发现支持对电动汽车充电基础设施的有效规划和设计.
- 结果为电动汽车充电服务的定价策略和运营成本提供了信息.
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