电动汽车充电场站经营充电量预测数据

本数据通过整合充电场站运营数据、商户及城市等级因子，构建了充电量预测模型，为各相关方提供了多维度的决策支持。具体应用场景如下：
1、对平台的价值（助力资源优化配置）：平台可通过经营充电量预测结果识别高需求场站和区域，从而建议桩企优先调配维护资源，建议商家扩建充电桩。
2、对场站商户的价值：（1）运营效率提升：基于经营充电量预测值设置场站工作人员数量，提升服务质效。（2）投资回报分析：可结合预测结果，评估扩建充电桩或升级设备的投资可行性。
3、对城市管理的价值（能源电网协同）：可将充电量预测值同步至电网公司，为优化电力调度提供参考，避免局部电网过载。
4、对金融机构与投资者的价值：可通过预测值评估充电场站资产价值，为融资、并购或REITs（不动产投资信托）提供数据支撑。1.数据采集：原始数据经授权合法获取并使用，采集站点ID、商户ID、商户等级（根据商户历史经营规模排序进行赋级）、城市名称、城市等级（根据城市充电场站经营规模排序进行赋级）、统计日期、离线总分钟数、离线影响充电量的预估值/kwh、离线时长占比等字段。
2.设置影响因子：（1）商户等级因子β：若商户等级为A级，则赋值1.14，B级则赋值1.07，C级则赋值1，D级则赋值0.93。（2）城市等级因子γ：若城市等级为S级，则赋值1.21，A级则赋值1.07，B级则赋值0.95。
3.建立经营充电量预测模型：（1）基准经营充电量预测：基准经营充电量预测值=（离线影响充电量的预估值÷离线总分钟数）÷离线时长占比。（2）修正计算：修正后的经营充电量预测值=基准经营充电量预测值×商户等级因子β×城市等级因子γ。

This dataset integrates operating data of charging stations, merchant and city level factors to construct a charging volume prediction model, providing multi-dimensional decision support for relevant stakeholders. The specific application scenarios are as follows:
1. Value for the platform (facilitating optimal resource allocation): The platform can identify high-demand stations and regions based on the predicted charging volume results, thereby recommending that charging infrastructure enterprises prioritize the allocation and maintenance of resources, and advising merchants to expand charging piles.
2. Value for station merchants:
(1) Operational efficiency improvement: Adjust the number of station staff based on the predicted operating charging volume to improve service quality and efficiency.
(2) Investment return analysis: Evaluate the investment feasibility of expanding charging piles or upgrading equipment by combining the prediction results.
3. Value for urban management (energy-grid coordination): Sync the predicted charging volume data to power grid companies, providing reference for optimizing power dispatch and avoiding local grid overload.
4. Value for financial institutions and investors: Evaluate the asset value of charging stations through the prediction results, providing data support for financing, mergers and acquisitions, or REITs (Real Estate Investment Trust).

1. Data collection: Original data is legally obtained and used with authorization. Collected fields include: station ID, merchant ID, merchant level (graded based on the ranking of merchants' historical operating scale), city name, city level (graded based on the ranking of the operating scale of urban charging stations), statistical date, total offline minutes, estimated offline impact on charging volume/kWh, offline duration proportion, etc.
2. Setting influence factors:
(1) Merchant level factor β: Assign a value of 1.14 for Grade A merchants, 1.07 for Grade B, 1 for Grade C, and 0.93 for Grade D.
(2) City level factor γ: Assign a value of 1.21 for Grade S cities, 1.07 for Grade A cities, and 0.95 for Grade B cities.
3. Establishing the operating charging volume prediction model:
(1) Benchmark operating charging volume prediction: Benchmark predicted operating charging volume = (Estimated offline impact on charging volume ÷ total offline minutes) ÷ offline duration proportion.
(2) Correction calculation: Corrected predicted operating charging volume = Benchmark predicted operating charging volume × merchant level factor β × city level factor γ.

搜集汇总

背景与挑战

背景概述

该数据集为电动汽车充电场站经营充电量预测数据，包含2068条记录，每日更新，用于预测充电量，支持资源优化配置、运营效率提升、能源电网协同和投资决策等应用场景。