Open AccessIntermittent Prediction Method Based On Marcov Method And Grey Prediction Method
Author(s) -
Aiping Jiang,
Junjun Gao,
Ying Wan,
Xinyi Zhao,
Siqi Shan
Publication year - 2016
Publication title -
european scientific journal
Language(s) - English
Resource type - Journals
eISSN - 1857-7881
pISSN - 1857-7431
DOI - 10.19044/esj.2016.v12n15p81
Subject(s) - demand forecasting , supply and demand , power demand , sequence (biology) , interval (graph theory) , peak demand , time sequence , demand patterns , market demand schedule , computer science , on demand , dynamic demand , product (mathematics) , econometrics , power (physics) , operations research , economics , demand management , mathematics , data mining , engineering , microeconomics , electricity , power consumption , macroeconomics , biology , genetics , geometry , quantum mechanics , multimedia , physics , combinatorics , electrical engineering
This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the demand quantity in subsequent n intervals using Grey prediction model GM (1, 1) based on the sequence of demand quantity. Finally, the author places the demand quantity in the n intervals in order at the moments where demand occurs to get the predicted result of demand for electric material with intermittent demand. According to instance analysis, the integrated approach mentioned in this paper surpasses existing methods in providing accurate prediction on data of product with intermittent demand.