Forecast Energy Consumption Time-Series Dataset using Multistep LSTM Models


Nazir, S. and Abd. Aziz, Azlan and Hossen, Md. Jakir and Ab Aziz, Nor Azlina and Murthy, G. Ramana (2021) Forecast Energy Consumption Time-Series Dataset using Multistep LSTM Models. Journal of Physics: Conference Series, 1933 (1). 012054. ISSN 1742-6588

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Smart grid and smart metering technologies allow residential consumers to monitor and control electricity consumption easily. The real-time energy monitoring system is an application of the smart grid technology used to provide users with updates on their home electricity consumption information. This paper aims to forecast a month ahead of daily electricity consumption time-series data for one of the real-time energy monitoring system named beLyfe. Four separate multistep Long Short Term Memory LSTM neural network sequence prediction models such as vanilla LSTM, Bidirectional LSTM, Stacked LSTM, and Convolutional LSTM ConvLSTM has been evaluated to determine the optimal model to achieve this objective. A comparison experiment is performed to evaluate each multistep LSTM model performance in terms of accuracy and robustness. Experiment results show that the ConvLSTM model achieves overall high predictive accuracy and is less computationally expensive during model training than remaining models.

Item Type: Article
Uncontrolled Keywords: Smart power grids
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3001-3521 Distribution or transmission of electric power
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 04 Oct 2021 04:28
Last Modified: 04 Oct 2021 04:28


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