摘要
省级或市级的供电公司在年末或年初需要预测新的一年的配网物资需求,以往凭经验的估算方法精度差效率低下,本文用大数据的方法对物资进行预测,在获取ERP系统中的物资领料数据后,把物资按用途和种类分成几十种标包,然后把各标包的领料金额时间序列除以对应年份的投资额得到月粒度的单位投资额的领料时间序列,在对单位投资额的领料时间序列进行数据平滑和异常值处理后,用LSTM长短记忆神经网络、Croston、一次指数平滑和二次指数平滑等经典算法对序列进行预测,最后用年粒度的单位投资额的领料金额序列进行加权修正得到预测的物资需求。本文提出的方法在浙江、江苏、福建、四川四省以及浙江绍兴所辖4个地区的物资数据上进行测试,得到较为满意的预测效果。
Provincial or municipal power supply companies need to forecast the distribution network material demand in the new year at the end of the year or at the beginning of the year.The previous estimation method based on experience has poor accuracy and low efficiency.After obtaining the material claim data in the ERP system,the materials are divided into dozens of standard packages according to the use and type,then,dividing the time series of the amount of material claim of each package by the investment amount of the corresponding year,the time series of material claim of the unit investment amount of the monthly granularity can be obtained,classical algorithms such as LSTM long-short memory neural network,Croston,primary exponential smoothing and secondary exponential smoothing are used to predict the sequence.At last,the material demand forecasting result is obtained by using the weighted correction from the series of annual material demand of unit investment.The method proposed in this paper is tested on the data of Zhejiang,Jiangsu,Fujian,Sichuan and Shaoxing,and the results are satisfactory.
作者
沈澄泓
周长星
胡世通
王传祯
SHEN Chenghong;ZHOU Changxing;HU Shitong;WANG Chuanzhen(Zhejiang Fanhai Zhixing Power Technology Co.,Ltd.,Hangzhou 310052 Zhejiang,China;State Grid Shaoxing Power Supply Company,Shaoxing,312099 Zhejiang,China)
出处
《电力大数据》
2021年第5期18-25,共8页
Power Systems and Big Data
关键词
配网
标包
长短期记忆神经网络
克罗斯顿法
指数平滑
distribution network
package
long short term memory networks
croston
exponential smoothing
