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

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Comparative analysis of hybrid CNN-LSTM models improves energy consumption forecasting accuracy, highlighting smart grid benefits.

Hybrid CNN-LSTM models enhance forecasting accuracy in energy consumption, improving load management strategies.
Results indicate hybrid models achieve a better performance metric compared to standalone LSTM and GRUs using historical data.
Assessment employed symmetric Mean Absolute Percentage Error (sMAPE) and Root Mean Square Error (RMSE) for accuracy.
This research underscores the importance of advanced forecasting in optimizing smart grid efficiency and reliability.

Nandigam et al. (2025) studied this question.

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View Full Paper

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Authors

Comparative analysis of hybrid CNN-LSTM models improves energy consumption forecasting accuracy, highlighting smart grid benefits.

Hybrid CNN-LSTM models enhance forecasting accuracy in energy consumption, improving load management strategies.
Results indicate hybrid models achieve a better performance metric compared to standalone LSTM and GRUs using historical data.
Assessment employed symmetric Mean Absolute Percentage Error (sMAPE) and Root Mean Square Error (RMSE) for accuracy.
This research underscores the importance of advanced forecasting in optimizing smart grid efficiency and reliability.

Nandigam et al. (2025) studied this question.

View Full Paper

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Bookmark