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Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data

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  • Jaewon Chung
  • Beakcheol Jang

Abstract

The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas.

Suggested Citation

  • Jaewon Chung & Beakcheol Jang, 2022. "Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-16, November.
  • Handle: RePEc:plo:pone00:0278071
    DOI: 10.1371/journal.pone.0278071
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    References listed on IDEAS

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    1. Arora, Vipin & Lieskovsky, Jozef, 2016. "Electricity Use as an Indicator of U.S. Economic Activity," EconStor Research Reports 126147, ZBW - Leibniz Information Centre for Economics.
    2. Hassan Haes Alhelou & Mohamad Esmail Hamedani-Golshan & Takawira Cuthbert Njenda & Pierluigi Siano, 2019. "A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges," Energies, MDPI, vol. 12(4), pages 1-28, February.
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    1. Wang, Chao-fan & Liu, Kui-xing & Peng, Jieyang & Li, Xiang & Liu, Xiu-feng & Zhang, Jia-wan & Niu, Zhi-bin, 2025. "High-precision energy consumption forecasting for large office building using a signal decomposition-based deep learning approach," Energy, Elsevier, vol. 314(C).

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