Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment
Joint Authors
Li, Chuanbin
Zheng, Xiaosen
Yang, Zikun
Kuang, Li
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-06
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability.
Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals.
In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment.
By taking the advantages of Fog Computing framework, we first propose a prototype-based clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users’ historical records of electricity consumption and identifying the most important features.
Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user’s electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used.
The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.
American Psychological Association (APA)
Li, Chuanbin& Zheng, Xiaosen& Yang, Zikun& Kuang, Li. 2018. Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1216058
Modern Language Association (MLA)
Li, Chuanbin…[et al.]. Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1216058
American Medical Association (AMA)
Li, Chuanbin& Zheng, Xiaosen& Yang, Zikun& Kuang, Li. Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1216058
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1216058