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http://dx.doi.org/10.7471/ikeee.2019.23.1.134

Prediction for Energy Demand Using 1D-CNN and Bidirectional LSTM in Internet of Energy  

Jung, Ho Cheul (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Sun, Young Ghyu (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Lee, Donggu (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Kim, Soo Hyun (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Hwang, Yu Min (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Sim, Issac (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Oh, Sang Keun (Dept. of Power Electronics, PLASPO Co., Ltd.)
Song, Seung-Ho (Dept. of Electric Engineering, Kwangwoon University)
Kim, Jin Young (Dept. of Electronic Convergence Engineering, Kwangwoon University)
Publication Information
Journal of IKEEE / v.23, no.1, 2019 , pp. 134-142 More about this Journal
Abstract
As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.
Keywords
CNN; LSTM; 1D-ConvBLSTM; Energy prediction; Internet of Energy;
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