Neural Network-Based Electric Load Forecasting

This document presents a neural network-based electric load forecasting model developed from scratch. The implementation includes comprehensive datasets and fully executable code, allowing immediate deployment for electric load prediction tasks. The model leverages neural networks' pattern recognition capabilities to forecast future electricity demand based on historical load data. Key technical components involve data normalization, time-series feature engineering, and multilayer perceptron (MLP) architecture with backpropagation training. Through this model, users can analyze electricity consumption trends and make informed decisions for grid management. The system contributes to improved power supply efficiency, enhanced grid reliability, cost optimization, and resource conservation. The complete code structure includes main training scripts (train_model.py), prediction modules (predict.py), and data visualization utilities for result analysis. This implementation serves as a practical resource for power system researchers and engineers working on load forecasting applications.