RBF Neural Network for Short-Term Power System Load Forecasting

This program implements an RBF (Radial Basis Function) neural network specifically designed for short-term load forecasting in power systems. The algorithm processes historical power consumption data to predict future load demands, enabling more accurate power system planning and operational decisions. The RBF neural network architecture employs radial basis functions as activation functions in the hidden layer, utilizing weighted connections and bias terms to establish nonlinear relationships between input patterns and output predictions. Key implementation features include data normalization procedures to ensure consistent input scaling, Gaussian kernel functions for hidden layer transformations, and efficient weight optimization through gradient descent or pseudo-inverse methods. The program typically involves three main phases: historical data preprocessing (handling missing values and outlier detection), network training (centers selection and width parameter tuning), and load prediction (forward propagation through the trained network). By utilizing this implementation, users can gain deeper insights into power consumption patterns, forecast peak demand periods, and optimize grid operations through proactive load management strategies. The modular code structure allows for customization of network parameters, including the number of hidden neurons and training epochs, to adapt to specific regional load characteristics and forecasting requirements.