Data Prediction Using Elman Neural Networks - Research on Power Load Forecasting Models

This study aims to investigate the application of Elman neural networks in data prediction, specifically focusing on power load forecasting models. We conduct an in-depth analysis of the Elman neural network architecture, which features feedback connections from hidden layers to context layers, enabling dynamic temporal processing capabilities. The implementation involves using historical power load data as input sequences, where key algorithmic components include time-series preprocessing, network weight optimization through backpropagation through time (BPTT), and recursive prediction mechanisms. Through this research, we seek to provide new insights into Elman neural networks' performance in power load forecasting scenarios, including implementation considerations for handling seasonal patterns and peak demand periods. The findings are intended to offer valuable reference information for decision-makers in the power industry and related researchers working on energy demand prediction systems.