BP Neural Network for Load Forecasting

In this article, we provide a comprehensive introduction to the application of Backpropagation (BP) Neural Networks. BP neural networks have been widely adopted in load forecasting and electricity price estimation due to their capability to predict future outcomes by learning from historical data patterns. The implementation typically involves three key layers: input layer for feature normalization, hidden layers with activation functions (like sigmoid or ReLU), and output layer for final prediction generation. We systematically explain the role of each network component, including gradient descent optimization and error backpropagation algorithms that adjust weights through iterative training. Additionally, we provide sample datasets containing historical load/pricing data and visualizations of prediction results, enabling readers to intuitively observe the network's performance. Through detailed code structure explanations - such as parameter initialization, forward propagation calculations, and loss function minimization - we aim to help readers thoroughly understand both the theoretical principles and practical implementation of BP neural networks for real-world applications.