摘要
随着全国范围内工商业用户代理购电业务的逐步推广,对电网企业而言,如何做好代理购电电量预测、量价费管理等工作,对保障代理购电用户能平稳有序地用电意义重大。本文以兰州市代理购电历史数据为研究对象,通过代理购电现状分析、用户电量影响因素研究、售电量及电费收入预测模型构建、量价费管理建议及分析方法研究,为电量预测模型建立了切实可行的指标体系、构建了基于深度学习的用电量及电费收入预测模型、实现了大工业用户代理购电用电量及电费收入的拟合预测、规范了量价费分析维度及内容。这有助于提升公司电量预测准确率,支撑代理购电业务数字化的高效开展,并持续优化量价费管理水平。
With the gradual promotion of the agency power purchase business of industrial and commercial users nationwide,it is of great significance for power grid enterprises to do a good job in forecasting the amount of power purchased by the agency,and managing the quantity,price and fee to ensure that the agency power purchase users can use electricity in a smooth and orderly manner.This paper takes the historical data of power purchase by agent in Lanzhou City as the research object,through the analysis of the current situation of power purchase by agent,the study of factors influencing the user’s power consumption,the construction of power sales and tariff revenue prediction model,the proposal of volume,price and tariff management and the research of the analysis method,it establishes a practicable index system for the power prediction model,constructs the prediction model for power consumption and tariff revenue based on the deep learning,and realizes the fitting prediction of power consumption and tariff revenue for the agent of purchasing power for large industrial users.and tariff revenue of large industrial users,and standardized the dimensions and contents of the volume,price and tariff analysis.This helps to improve the accuracy of the Company’s power consumption prediction,support the efficient development of the digitalization of the agency power purchase business,and continuously optimize the management level of volume,price and fee.
作者
韩晔飞
石雅婷
周霖
王擎
王文华
HAN Yefei;SHI Yating;ZHOU Lin;WANG Qing;WANG Wenhua(State Grid Lanzhou Power Supply Company,Lanzhou Gansu 730000,China)
出处
《信息与电脑》
2024年第19期21-24,共4页
Information & Computer
关键词
代理购电
电量预测
电费收入预测
量价费管理
agent power purchase
electricity quantity forecast
electricity bill income forecast
quantity and price management
