武汉市冬季低温场景充电量预测数据

本数据通过分析武汉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=0.016×(5-T)+0.018H 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=0.020×(3−T)+0.014H 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+0.28H） 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=0.85；否则ε=1.0）。"This dataset analyzes the charging attenuation characteristics of different types of power batteries under the low-temperature winter environment in Wuhan City, to provide charging volume prediction data for decision support in charging operation management. Its main applications are as follows: 1. Guide charging operators to predict the charging demand under low-temperature conditions by combining meteorological parameters such as temperature, humidity and wind speed with the attenuation rate characteristics of ternary lithium-ion batteries and lithium iron phosphate batteries; 2. Ensure the stability of charging services under extreme weather conditions; 3. Provide load forecasting reference for power dispatching departments to ensure the stable operation of the power grid during low-temperature periods; 4. Provide accurate charging duration estimation for new energy vehicle (NEV) users, thereby improving the charging experience in low-temperature environments. 1. Data Collection and Processing Collect data from the enterprise's own charging pile equipment management system, including charging station ID, city name, prediction date, temperature T, humidity H, wind speed W, average daily charging volume Q in the past 30 days, proportion of orders for vehicles equipped with ternary lithium-ion batteries in the past 30 days (P_ternary), and proportion of orders for vehicles equipped with lithium iron phosphate batteries in the past 30 days (P_lfp). Clean the collected data by removing records where the temperature exceeds 8℃, which are non-low-temperature scenarios. 2. Core Calculations Calculate the attenuation rate via feature engineering: ① Attenuation rate α of ternary lithium-ion batteries: When T < 5℃: α = 0.016 × (5 - T) + 0.018 × H When T ≥ 5℃: α = 0 (no attenuation) ② Attenuation rate β of lithium iron phosphate batteries: When T < 3℃: β = 0.020 × (3 - T) + 0.014 × H When T ≥ 3℃: β = 0 (no attenuation) ③ Comprehensive attenuation rate γ: γ = [P_ternary × α + P_lfp × β] / (1 + 0.28 × H) 3. Establishment of Charging Volume Prediction Model Establish the charging volume prediction model: Predicted charging volume Q_pred = Q × (1 + γ) × ε, where ε is the weather impact coefficient, which is determined by the wind speed W: ε = 0.85 when W ≥ 6 Beaufort scale levels, otherwise ε = 1.0.

搜集汇总

背景与挑战

背景概述

该数据集专注于武汉市冬季低温场景下的充电量预测，包含500条记录，每月更新。它通过整合温度、湿度、风速等气象参数，以及三元锂电池和磷酸铁锂电池的衰减率特征，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，并为电力调度部门提供负荷预测参考。数据集以xlsx格式存储，涵盖充电站编号、预测日期、预测充电量等14个字段，适用于新能源汽车充电管理和电网优化场景。