What question did this study set out to answer?

The aim is to enhance accuracy in forecasting electricity consumption for residential and commercial sectors in the Mediterranean region.

A Novel deep learning model to improve electric energy consumption forecasting accuracy for lighting, residential, and commercial loads in the mediterranean region

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates improved forecasting accuracy in the Mediterranean region, indicating better energy management.

The aim is to enhance accuracy in forecasting electricity consumption for residential and commercial sectors in the Mediterranean region.
Developed deep learning models (CNN, LSTM, hybrid CNN-LSTM) using monthly data from 2016 to 2023.
Optimized hyperparameters via grid search including batch size and network structure.
Evaluated model performance using RMSE, MAE, MAPE, and R² metrics.
The hybrid CNN-LSTM model outperformed standalone CNN and LSTM models across all load types, achieving higher accuracy.
Model performance evaluations showed significant improvements in forecasting reliability with better generalization capabilities.
The proposed framework enhances decision-making in energy management and planning for sector-based electricity load forecasting.

Yavuzdeğer et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates improved forecasting accuracy in the Mediterranean region, indicating better energy management.

The aim is to enhance accuracy in forecasting electricity consumption for residential and commercial sectors in the Mediterranean region.
Developed deep learning models (CNN, LSTM, hybrid CNN-LSTM) using monthly data from 2016 to 2023.
Optimized hyperparameters via grid search including batch size and network structure.
Evaluated model performance using RMSE, MAE, MAPE, and R² metrics.
The hybrid CNN-LSTM model outperformed standalone CNN and LSTM models across all load types, achieving higher accuracy.
Model performance evaluations showed significant improvements in forecasting reliability with better generalization capabilities.
The proposed framework enhances decision-making in energy management and planning for sector-based electricity load forecasting.

Yavuzdeğer et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark