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http://dx.doi.org/10.7471/ikeee.2019.23.1.120

Prediction of Power Consumptions Based on Gated Recurrent Unit for Internet of Energy  

Lee, Dong-gu (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Sun, Young-Ghyu (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Sim, Is-sac (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Hwang, Yu-Min (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Kim, Sooh-wan (Co. Gridwiz)
Kim, Jin-Young (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Publication Information
Journal of IKEEE / v.23, no.1, 2019 , pp. 120-126 More about this Journal
Abstract
Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.
Keywords
Machine Learning; Deep Learning; RNN; GRU; Demand Forecasting;
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