青岛市冬季低温场景充电量预测数据

本数据通过分析青岛市冬季低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、湿度和风速等气象参数，结合三元锂电池和磷酸铁锂电池的衰减率特征，预测低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，确保电网在低温时段的稳定运行。同时可为新能源汽车用户提供准确的充电时长预估，提升低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、风速W、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＞8℃的非低温场景记录。 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＜5℃ 时：α=0.014×(5-T)+0.016H 当T≥5℃ 时：α=0（不衰减） ②磷酸铁锂电池衰减率β： 当T＜3℃ 时：β=0.018×(3−T)+0.012H 当T≥3℃ 时：β=0（不衰减） ③综合衰减率γ： γ=[P（三元）×α+P（铁锂）×β]/（1+0.24H） 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，根据风速W取值（当W≥6级时，ε=0.89；否则ε=1.0）。"This dataset analyzes the charging decay characteristics of various power battery types under the low-temperature environment of Qingdao during winter, providing charging volume prediction data to support decision-making for charging operation management. Its core applications include: guiding operators to forecast charging demand under low-temperature conditions by integrating meteorological parameters such as temperature, humidity and wind speed with the decay rate features of ternary lithium batteries and lithium iron phosphate batteries; ensuring the stability of charging services during extreme weather; providing load forecasting references for power dispatching departments to guarantee the stable operation of the power grid in low-temperature periods; and offering accurate charging duration estimation for new energy vehicle users, thereby enhancing the charging experience in low-temperature environments. 1. Data Collection and Processing Collect management data from the enterprise's self-owned charging pile facilities, including charging station ID, city name, prediction date, temperature T, humidity H, wind speed W, average daily charging volume over the past 30 days Q, order share of ternary lithium battery vehicles over the past 30 days P(ternary), and order share of lithium iron phosphate battery vehicles over the past 30 days P(iron phosphate), etc. Clean the collected data and remove records of non-low-temperature scenarios where the temperature exceeds 8℃. 2. Core Calculation Calculate the decay rate through feature engineering: ① Ternary lithium battery decay rate α: When T < 5℃: α = 0.014 imes(5-T) + 0.016H When T ≥ 5℃: α = 0 (no decay) ② Lithium iron phosphate battery decay rate β: When T < 3℃: β = 0.018 imes(3-T) + 0.012H When T ≥ 3℃: β = 0 (no decay) ③ Comprehensive decay rate γ: γ = [P(ternary) imesα + P(iron phosphate) imesβ]/(1 + 0.24H) 3. Establish Charging Volume Prediction Model Establish a charging volume prediction model: Predicted charging volume Q(predicted) = Q imes(1+γ) imesε. Where ε is the weather impact coefficient, which is determined based on the wind speed W: when W ≥ level 6, ε=0.89; otherwise ε=1.0.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于青岛市冬季低温环境下的充电量预测，包含500条记录，每月更新，涵盖温度、湿度、风速、电池衰减率等关键字段。它通过分析三元锂电池和磷酸铁锂电池的衰减特性，结合气象参数，构建预测模型，旨在为充电运营商提供决策支持、保障极端天气服务稳定性，并为电力调度和用户充电体验优化提供参考。