Data Prediction with Elman Neural Network - Power Load Forecasting Model Research

In this article, we explore the application of Elman neural networks for data prediction, specifically focusing on power load forecasting models. We begin by introducing fundamental concepts and principles of neural networks and Elman neural networks, along with their applications in data prediction. The discussion then delves into power load forecasting model research, covering data collection, data preprocessing (including normalization techniques), model training (using backpropagation through time), and testing methodologies. We examine optimization strategies for Elman neural networks to improve prediction accuracy, such as adjusting hidden layer neurons and tuning learning rates using gradient descent algorithms. The article also discusses potential application scenarios and future research directions. Through this study, readers will gain deeper insights into Elman neural network implementations for data prediction and master essential techniques for power load forecasting model development, including practical code structures for handling time-series data and evaluating prediction performance using metrics like RMSE and MAE.