Improved Giza pyramids construction algorithm for Modify the deep neural network-based method for energy demand...

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Computational Science

Background:

• Accurate energy demand prediction is vital for optimizing energy systems, reducing costs, and enhancing service reliability.
• Deep Neural Networks (DNNs) are effective for energy forecasting but can be sensitive to data quality and hyperparameter selection.
• Existing forecasting models like modified BP neural network (MBPNN), Neural Network based Genetic Algorithm (NNGA), and reinforcement learning and deep neural network (RLDNN) have limitations.

Purpose of the Study:

• To propose a novel and more reliable technique for energy demand forecasting by integrating DNNs with an enhanced Giza pyramid construction methodology.
• To optimize DNN hyperparameter selection for improved forecasting accuracy and effectiveness.
• To evaluate the proposed method against state-of-the-art models using real-world energy demand data.

Main Methods:

• Developed an integrated approach combining Deep Neural Networks (DNNs) for capturing complex variable relationships and an enhanced Giza pyramid construction algorithm (IGPCA) for optimal hyperparameter tuning.
• Utilized real-world energy demand data for model evaluation.
• Benchmarked the proposed IGPCA/DNN method against MBPNN, NNGA, and RLDNN models.

Main Results:

• The proposed IGPCA/DNN method demonstrated superior energy prediction accuracy across short-term, medium-term, and long-term forecasting horizons.
• Achieved lower Mean Squared Error (MSE) scores: 0.564 (short-term), 0.587 (medium-term), and 0.629 (long-term), outperforming baseline models.
• The enhanced Giza pyramid construction methodology effectively optimized DNN hyperparameters, leading to improved forecasting performance.

Conclusions:

• The novel integration of DNNs and the enhanced Giza pyramid construction methodology provides a more reliable and effective solution for energy demand forecasting.
• The proposed approach significantly outperforms existing state-of-the-art models in terms of prediction accuracy and reliability.
• Accurate energy demand forecasting is crucial for optimizing energy system operations and reducing associated costs.