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August 25, 2025Journal of Renewable Energy and Smart Grid Technology

Hybrid Deep Learning Models for Energy Consumption Forecasting: A CNN-LSTM Approach for Large-Scale Datasets

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Authors

SNSri Harish NandigamHindustan Institute of Technology and ScienceKNK. NageswararaoGuntur Medical CollegePSPurnima K. SharmaChitkara University

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Implication

Comparative analysis reveals hybrid CNN-LSTM models improve energy consumption forecasting in smart grids, suggesting better load management strategies.

Key Points

  • Hybrid cnn-lstm models achieve improved accuracy in forecasting energy consumption, indicating more effective strategies for energy management.
  • The analysis shows a notable reduction in forecasting errors compared to standalone models, with sMAPE, Loss, and RMSE metrics confirming the results.
  • Using complex historical time series data, hybrid models efficiently analyze patterns in energy consumption, providing actionable insights for smart grids.
  • This study contributes to enhanced operational efficiency in energy systems, supporting the reliability of smart grids and their management.

Cite This Study

Nandigam et al. (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae1430https://doi.org/10.69650/rast.2025.261326
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