Forecast Energy Consumption Time-Series Dataset using Multistep LSTM Models. Issue 1 (June 2021)

• Record Type: Journal Article
• Authors: Nazir, S.; Aziz, Azlan Ab; Hosen, J.; Aziz, Nor Azlina; Ramana Murthy, G.
• Is Part Of: Journal of physics. Volume 1933:Issue 1(2021)
• Publication Date: 2021-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1933/1/012054
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.