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الرئيسية > An Adaptive Framework For Real...>الوصف

An Adaptive Framework For Real Time Data Reduction In AMI

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Mohamed, Marwa F. (Author)
مؤلفين آخرين: Shabayek, Abd El-Rahman (Co-Author) , El-Gayyar, Mahmoud (Co-Author) , Nassar, Hamed (Co-Author)
المجلد/العدد: مج31, ع3
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 392 - 402
DOI: 10.33948/0584-031-003-012
ISSN: 1319-1578
رقم MD: 974710
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
علوم الحاسوب | قواعد البيانات | الخوارزميات
كلمات المؤلف المفتاحية:
Real-Time Data Reduction | Forecasting Methods | Advanced Metering Infrastructure (AMI) | Decision Tree Algorithms | Cloud
رابط المحتوى:
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المستخلص: In existing Advanced Metering Infrastructure (AMI), data collection intervals for each smart meter (SM) typically vary from 15 to 60 min. If we have 1 million SMs that transmit data every 15 min, these SMs will export 4 million records per hour. This leads to dramatically increasing bandwidth usage, energy consumption, traffic cost and I/O congestion. In this work, we present an adaptive framework for minimizing the amount of data transfer from SMs. The reduction in the framework is forecasting based; when an SM reading is close to the forecasted value, the SM does not transmit the reading. In order for the framework to be adaptive to the ever-changing pattern of SM data, it is provided with a pool of forecasting methods. A supervised-learning scheme is employed to switch in real-time to the forecasting method most suitable to the current data pattern. The experimental results demonstrate that the proposed framework achieves data reduction rates up to 98% with accuracy 96%, depending on the operational parameters of the framework and consumer behavior (statistical features of SM data). ©2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ISSN: 1319-1578

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