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Deep learning-based household electric energy consumption forecasting

Content Provider	IET Digital Library
Author	Hyeon, Jonghwan Lee, Hye Young Ko, Bowon Choi, Ho Jin
Abstract	With the advent of various electronic products, the household electric energy consumption is continuously increasing, and therefore it becomes very important to predict the household electric energy consumption accurately. Energy prediction models also have been developed for decades with advanced machine learning technologies. Meanwhile, the deep learning models are still actively under study, and many newer models show the state-of-the-art performance. Therefore, it would be meaningful to conduct the same experiment with these new models. Here, the authors predict the household electric energy consumption using deep learning models, known to be suitable for dealing with time-series data. Specifically, vanilla long short-term memory (LSTM), sequence to sequence, and sequence to sequence with attention mechanism are used to predict the electric energy consumption in the household. As a result, the vanilla LSTM shows the best performance on the root-mean-square error metric. However, from a graphical point of view, it seems that the sequence-to-sequence model predicts the energy consumption patterns best and the vanilla LSTM does not follow the pattern well. Also, to achieve the best performance of each deep learning model, vanilla LSTM, sequence to sequence, and sequence to sequence with attention mechanism should observe past 72, 72, and 24 h, respectively.
Starting Page	639
Ending Page	642
Page Count	4
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1219
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-31
Access Restriction	Open
Rights License	Creative Commons Attribution-No Derivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Subject Keyword	Advanced Machine Learning Technology Deep Learning Model Energy Consumption Pattern Energy Prediction Model Household Electric Energy Consumption Forecasting Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Load Forecasting Mean Square Error Method Neural Computing Technique Numerical Analysis Power Consumption Power Engineering Computing Power System Planning And Layout Recurrent Neural Nets Root-mean-square Error Metric Sequence to Sequence with Attention Mechanism Sequence-to-sequence Model Statistics Time 24 H Time 72.0 Hour Time Series Time-series Data Vanilla Long Short-term Memory Vanilla LSTM
Content Type	Text
Resource Type	Article

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