A hybrid LSTM neural network for energy consumption forecasting of individual households

Journal Publication ResearchOnline@JCU
Yan, Ke;Li, Wei;Ji, Zhiwei;Qi, Meng;Du, Yang
Abstract

Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the 'UK-DALEat project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics.

Journal

IEEE Access

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Volume

7

ISBN/ISSN

2169-3536

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Pages Count

10

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Publisher

Institute of Electrical and Electronics Engineers

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EISSN

N/A

DOI

10.1109/ACCESS.2019.2949065