杭州市夏季高温场景充电量预测数据

本数据通过分析杭州市夏季高温环境下不同类型动力电池的充电衰减特性，为充电运营管理提供决策支持提供充电量数据预测。主要应用于：指导运营商根据温度、湿度和天气状况等气象参数，结合三元锂电池和磷酸铁锂电池的衰减率特征，预测高温条件下的充电需求；在极端天气条件下保障充电服务稳定性；为电力调度部门提供负荷预测参考，确保电网在高温时段的稳定运行。同时可为新能源汽车用户提供准确的充电时长预估，提升高温环境下的充电体验。 "1.数据采集与处理 采集企业自有充电桩设备管理数据，包括充电站编号、城市名称、预测日期、温度T、湿度H、天气状况、近30天日均充电量Q、近30天三元锂电池车辆订单占比P（三元）、近30天磷酸铁锂电池车辆订单占比P（铁锂）等数据。对采集的数据进行清洗，剔除温度＜25℃的非高温场景记录。 2.核心计算 通过特征工程计算衰减率： ①三元锂电池衰减率α： 当T＞35℃ 时：α=0.018×(T-35)+0.015H 当T≤35℃ 时：α=0（不衰减） ②磷酸铁锂电池衰减率β： 当T＞40℃ 时：β=0.015×(T-40)+0.012H 当T≤40℃ 时：β=0（不衰减） ③综合衰减率γ： γ=[P（三元）×α+P（铁锂）×β]/（1+0.25H） 3.建立充电量预测模型 建立充电量预测模型：预测充电量Q（预测）=Q×(1+γ)×ε。其中ε为天气影响系数，根据天气状况取值（晴天：0.95；多云：0.98；阴/雨天：1.0）。"This dataset analyzes the charging decay characteristics of various power batteries under high-temperature summer environments in Hangzhou, providing charging volume prediction data to support decision-making for charging operation management. Its core applications are as follows: guiding operators to predict charging demand under high-temperature conditions based on meteorological parameters such as temperature, humidity and weather conditions, combined with the decay rate features of ternary lithium batteries and lithium iron phosphate batteries; ensuring the stability of charging services under extreme weather; providing load prediction references for power dispatch departments to guarantee the stable operation of the power grid during high-temperature periods; and offering accurate charging duration estimates for new energy vehicle users, thereby enhancing their charging experience in high-temperature environments. 1. Data Collection and Processing Collect enterprise-owned charging pile equipment management data, including charging station ID, city name, prediction date, temperature T, humidity H, weather conditions, 30-day average daily charging volume Q, 30-day order proportion of vehicles equipped with ternary lithium batteries P (ternary), 30-day order proportion of vehicles equipped with lithium iron phosphate batteries P (LFP), and other relevant data. Clean the collected data by eliminating records of non-high-temperature scenarios where the temperature is lower than 25℃. 2. Core Calculations Calculate the decay rate through feature engineering: ① Decay rate α of ternary lithium batteries: When T > 35℃: α = 0.018 × (T - 35) + 0.015H When T ≤ 35℃: α = 0 (no decay) ② Decay rate β of lithium iron phosphate batteries: When T > 40℃: β = 0.015 × (T - 40) + 0.012H When T ≤ 40℃: β = 0 (no decay) ③ Comprehensive decay rate γ: γ = [P(ternary) × α + P(LFP) × β] / (1 + 0.25H) 3. Establishment of Charging Volume Prediction Model Establish the charging volume prediction model: Predicted charging volume Q(predicted) = Q × (1 + γ) × ε. Here, ε is the weather impact coefficient, with values assigned based on weather conditions (sunny: 0.95; cloudy: 0.98; overcast/rainy: 1.0).

搜集汇总

背景与挑战

背景概述

该数据集聚焦于杭州市夏季高温环境下的充电量预测，包含500条记录，每月更新。它整合了充电站基础信息、温度、湿度、天气状况等气象数据，以及三元锂电池和磷酸铁锂电池的订单占比和衰减率特征，通过特定算法模型预测高温条件下的充电需求。数据主要用于支持充电运营商优化管理、保障极端天气服务稳定性，并为电力调度和用户充电体验提供决策参考。