Retracted Article: High Performance Energy Prediction using Hadoop with Spark

Hung Duong-Ngoc,Tam Nguyen-Minh,Hoan Nguyen-Thanh

doi:10.1051/itmconf/20182002012

Abstract

This paper has been formally withdrawn on ethical grounds because the article contains extensive and repeated instances of plagiarism. Web of Conferences treats all identified evidence of plagiarism in the published articles most seriously. Such unethical behaviour will not be tolerated under any circumstance.

Highlights

Modeling and forecasting electrical energy consumption has been a research area interested for more than a decade
Sensor-based approaches to energy forecasting rely on readings from sensors or smart meters and contextual information such as meteorological information or work schedules to infer future energy behaviour
We developed a method to predict the execution time of Hadoop using deep learning technique

Summary

Introduction

Modeling and forecasting electrical energy consumption has been a research area interested for more than a decade. The importance of measuring and collecting electricity data, together with recent advances in sensor technology, have led to the proliferation of smart meters that measure and communicate electricity consumption These smart meters measure electricity at intervals of an haft of hour or less in Ho Chi Minh City, whereas some sensor devices can measure consumption in real time. Sensor-based approaches to energy forecasting rely on readings from sensors or smart meters and contextual information such as meteorological information or work schedules to infer future energy behaviour Historical data such as temperature, day of the week, time of day, and energy consumption are fed into a machine learning model that learns from them and can forecast future energy consumption. The accuracy of these sensor based approaches is comparable or superior to traditional approaches based on modeling in depth the properties of a building [3]

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