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September 10, 2025DÜMF Mühendislik DergisiOpen Access

Intelligent Forecasting of Electric Energy Demand with Artificial Neural Networks

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Comparative analysis shows RNN models outperform LSTM and CNN, suggesting enhanced accuracy in energy forecasting.

RNN-based deep learning models achieve a lower Mean Absolute Percentage Error of 9%, enhancing forecasting accuracy.
The study employs multidimensional datasets including historical consumption, weather, and economic indicators.
Various time series modeling techniques were tested, demonstrating the effectiveness of artificial neural networks.
The developed framework supports energy suppliers in resource planning while mitigating operational risks.

Savar et al. (2025) studied this question.

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