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Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment
المؤلفون المشاركون
Li, Chuanbin
Zheng, Xiaosen
Yang, Zikun
Kuang, Li
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-05-06
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1216058
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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