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

Yan, Ke, Li, Wei, Ji, Zhiwei, Qi, Meng, and Du, Yang (2019) A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access, 7. pp. 157633-157642.

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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.

Item ID: 61244
Item Type: Article (Research - C1)
ISSN: 2169-3536
Keywords: energy consumption, forecasting, long short term memory, wavelet transform
Copyright Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Funders: Zhejiang Provincial Natural Science Foundation of China (ZPNSF), National Natural Science Foundation of China (NNSFC)
Projects and Grants: ZPNSF Grant LY19F020016, NNSFC Grant 61602431, NNSFC Grant 61850410531, NNSFC Grant 61902225
Date Deposited: 18 Dec 2019 07:59
FoR Codes: 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090607 Power and Energy Systems Engineering (excl Renewable Power) @ 80%
09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090608 Renewable Power and Energy Systems Engineering (excl Solar Cells) @ 20%
SEO Codes: 85 ENERGY > 8507 Energy Conservation and Efficiency > 850704 Residential Energy Conservation and Efficiency @ 100%
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