摘要
针对LEAP模型在能源需求预测和碳排放分析中面临的高数据门槛问题,提出一种简化数据处理的方法。通过直接调查目标部门的能源使用源头,收集年度能源消耗总量和品种两项关键数据,并利用标准煤折算系数简化输入;借助Python的pandas库进行数据预处理,实现自动化单位转换、清洗和汇总,提高数据处理效率。结果表明,该方法构建的LEAP模型与传统方法在能源需求和二氧化碳排放量预测上高度一致,误差率仅为0.00103%~0.00116%。该方法有效降低了LEAP模型的数据门槛要求,提高了建模效率,为节能规划和能源效率提升提供了参考。
Aiming at the problem of high data threshold faced by the LEAP model in energy demand forecasting and carbon emission analysis,a method for simplifying data processing is proposed.By directly investigating the energy usage sources of the target department,two key data,namely the total annual energy consumption and the types,are collected,and the input is simplified by using the standard coal conversion coefficient.Data preprocessing is carried out with the help of the pandas library of Python to achieve automated unit conversion,cleaning and summary,and improve the efficiency of data processing.The results show that the LEAP model constructed by this method is highly consistent with the traditional methods in the prediction of energy demand and carbon dioxide emissions,with an error rate of only 0.00103%to 0.00116%.This method effectively reduces the data threshold requirements of the LEAP model,improves the modeling efficiency,and provides a reference for energy-saving planning and energy efficiency improvement.
作者
陈柏宇
林晔
赵景瑞
宋莹艳
CHEN Boyu;LIN Ye;ZHAO Jingrui;SONG Yingyan
出处
《节能》
2025年第6期106-109,共4页
Energy Conservation
基金
辽宁省科技厅面上项目(项目编号:2024-MSLH-403)。
关键词
LEAP模型
节能规划
数据处理
数据门槛
环境评估
LEAP model
energy-saving planning
data processing
data threshold
environmental assessment
