Energy Demand Forcasting System

• Github URL: Project Link

Developed a hybrid deep learning model combining CNNs for feature extraction and LSTMs for temporal modeling to forecast real-time electricity demand with 96% accuracy.

Processed and analyzed a comprehensive dataset of energy consumption records, optimizing the model with hyperparameter tuning using Optuna. Incorporated dropout regularization and batch normalization to enhance model robustness.

The system was deployed using TorchServe for efficient, scalable real-time predictions, enabling seamless integration with energy distribution systems to improve operational efficiency.