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Hybrid LSTM-Graph Convolutional Neural Network with Wavelet Transform and Correlation Analysis for Electrical Demand Forecasting

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Abstract

Accurate electrical demand forecasting is essential for power system efficiency, renewable energy investment, and cost-effective electricity production. For electrical demand consumption time series forecasting, this article proposes a novel deep learning architecture, wavelet transform and correlation-based hybrid LSTM-GCNN, that integrates long short-term memory (LSTM) and graph convolutional neural network (GCNN) layers. A GCNN captures dynamically distributed features and temporal correlations from graph data generated by wavelet decomposition and correlation analysis. The temporal patterns of the electrical demand consumption time series are captured by an LSTM. The proposed hybrid LSTM-GCNN architecture is evaluated using Indian Northern Regional Load Despatch Centre (NRLDC) electrical demand consumption data from 2018–2021 with a 15-min resolution of states Uttar Pradesh (U.P.) and Jammu and Kashmir (J &K). Hybrid LSTM-GCNN outperforms ARIMA, LSTM-univariate, LSTM-convolutional neural network and LSTM-multivariate prediction algorithms in universality, reliability, and accuracy. The proposed hybrid LSTM-GCNN architecture offers an efficient and promising method for forecasting time series of electrical demand consumption.

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Acknowledgements

Authors acknowledge the institute fellowship received by the corresponding author during the PhD program offered by Department of Higher Education, Ministry of Education, Government of India.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar, 190006, Jammu and Kashmir, India

    Keerti Rawal & Aijaz Ahmad

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  1. Keerti Rawal
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  2. Aijaz Ahmad
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Contributions

Keerti Rawal: conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing—original draft, Aijaz Ahmad: conceptualization, project administration, resources, supervision, validation, visualization, writing—review and editing.

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Correspondence to Keerti Rawal.

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Rawal, K., Ahmad, A. Hybrid LSTM-Graph Convolutional Neural Network with Wavelet Transform and Correlation Analysis for Electrical Demand Forecasting. SN COMPUT. SCI. 5, 412 (2024). https://doi.org/10.1007/s42979-024-02765-w

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