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Open AccessArticle

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

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Digital Contents Research Institute, Sejong University, Seoul 05006, Korea
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City 736464, Vietnam
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School of Electrical Engineering, Korea University, Seoul 02841, Korea
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Department of Software, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4237; https://doi.org/10.3390/app9204237
Received: 12 September 2019 / Revised: 1 October 2019 / Accepted: 3 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Actionable Pattern-Driven Analytics and Prediction)
The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans. View Full-Text
Keywords: electric energy consumption prediction; energy management system; CNN; Bi-LSTM electric energy consumption prediction; energy management system; CNN; Bi-LSTM
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Le, T.; Vo, M.T.; Vo, B.; Hwang, E.; Rho, S.; Baik, S.W. Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM. Appl. Sci. 2019, 9, 4237.

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