Forecasting Electricity Transfer Metrics using Neural Networks

The energy industry's shift towards renewable sources and larger power grids has increased the complexity of electricity generation and consumption. Efficient grid management is essential for stability, performance, and cost reduction. Traditional methods for forecasting electricity transfer metrics often fall short due to their reliance on static models and historical data. This paper explores the use of neural networks to improve predictions of electricity transfer efficiency and duration. By leveraging deep learning, we aim to enhance forecast accuracy and reliability, thus optimizing grid operations and integrating renewable energy sources more effectively. We present a methodology for developing and evaluating neural network models, including dataset preparation and model design. Our analysis demonstrates the advantages of deep learning over traditional methods, contributing to the modernization of power grid management and the promotion of sustainable energy practices.