Title :
An Improved Combined Model for the Electricity Demand Forecasting
Author :
Wang, Huiting ; Zhu, Suling ; Zhao, Jing ; Li, Guanhong
Author_Institution :
Dept. of Math. & Stat., Xuchang Univ., Xuchang, China
Abstract :
In this paper, we propose an improved combined forecasting model integrates the merits of data pretreatment, combined model and Markov chain, known as Markov combined model. The moving average is used for the data pretreatment or determination of trend, combined model is designed for the trend forecasting, and the Markov chain is employed for modifying the forecasting results of combined model. The forecasting results of electricity demand provide valuable information for the supply chain management. Therefore, we apply the Markov combined model to forecast the electricity demand. The forecasting results testify that the proposed combined forecasting technique is an effective method.
Keywords :
Markov processes; load forecasting; supply chain management; Markov chain; data pretreatment; electricity demand forecasting; improved combined model; supply chain management; Biological system modeling; Data models; Electricity; Forecasting; Markov processes; Mathematical model; Predictive models; Combined model; Electricity demand; Forecasting;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
DOI :
10.1109/ICCIS.2010.34