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http://dx.doi.org/10.7471/ikeee.2018.22.3.829

Implementation of Smart Metering System Based on Deep Learning  

Sun, Young Ghyu (Dept. of Wireless Communications Engineering, KwangWoon University)
Kim, Soo Hyun (Dept. of Wireless Communications Engineering, KwangWoon University)
Lee, Dong Gu (Dept. of Wireless Communications Engineering, KwangWoon University)
Park, Sang Hoo (Dept. of Wireless Communications Engineering, KwangWoon University)
Sim, Issac (Dept. of Wireless Communications Engineering, KwangWoon University)
Hwang, Yu Min (Dept. of Wireless Communications Engineering, KwangWoon University)
Kim, Jin Young (Dept. of Wireless Communications Engineering, KwangWoon University)
Publication Information
Journal of IKEEE / v.22, no.3, 2018 , pp. 829-835 More about this Journal
Abstract
Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.
Keywords
smart grid; smart meter; deep learning; recurrent neural network; power consumption prediction;
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