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논문검색

Deep Learning-Based Smart Meter Wattage Prediction Analysis Platform

초록

영어

As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.

목차

Abstract
1. INTRODUCTION
2. TEST ENVIRONMENT DOMAIN
3. SYSTEM ARCHITECTURE
4. DATA ANALYSIS FORECAST
4.1 Current Status of Electricity Consumption for Housing
4.2 Daily power consumption forecast
5. CONCLUSION
REFERENCES

저자정보

  • Seonghoon Jang PhD student, Department of IT Convergence , Hansei University, Korea
  • Seung-Jung Shin Professor, Department of ICT Convergence, Hansei University, Korea

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