济宁市冬季低温场景充电量预测数据

本数据通过分析济宁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 low-temperature winter environments in Jining City, providing charging volume prediction to support decision-making for charging operation management. Its core applications are as follows: guiding operators to forecast charging demand under low-temperature conditions based on meteorological parameters including temperature, humidity and wind speed, combined with the decay rate features of ternary lithium batteries and lithium iron phosphate (LFP) 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 (NEV) users, thereby enhancing the charging experience in low-temperature environments.

1. Data Collection and Processing
Collect management data from the enterprise's own charging pile equipment, including charging station ID, city name, prediction date, temperature T, humidity H, wind speed W, average daily charging volume Q over the past 30 days, proportion of ternary lithium battery vehicle orders P_ternary over the past 30 days, and proportion of lithium iron phosphate battery vehicle orders P_LFP over the past 30 days. Clean the collected data by removing records of non-low-temperature scenarios where the temperature exceeds 8℃.

2. Core Calculations
Calculate the decay rate via feature engineering:
① Ternary lithium battery decay rate α:
When T < 5℃: α = 0.014 × (5 - T) + 0.016H
When T ≥ 5℃: α = 0 (no decay)
② Lithium iron phosphate (LFP) battery decay rate β:
When T < 3℃: β = 0.018 × (3 - T) + 0.012H
When T ≥ 3℃: β = 0 (no decay)
③ Comprehensive decay rate γ:
γ = [P_ternary × α + P_LFP × β] / (1 + 0.24H)

3. Charging Volume Prediction Model Establishment
Establish the charging volume prediction model: Predicted charging volume Q(pred) = Q × (1 + γ) × ε. Here, ε is the weather impact coefficient, which is determined based on wind speed W: ε = 0.89 when W ≥ level 6; otherwise ε = 1.0.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于济宁市冬季低温环境下的充电量预测，包含500条记录，每月更新一次。它通过整合温度、湿度、风速等气象数据，以及三元锂电池和磷酸铁锂电池的衰减率特征，构建预测模型来估算充电需求，旨在为充电运营商和电力部门提供决策支持，优化低温条件下的服务稳定性和电网运行。