Electricity Load & Price Forecasting Using Deep Learning

Abstract

Forecasting electricity prices and loads is essential for grid stability and efficient energy management. This study presents a novel technique to increase short-term load accuracy. and pricing projections by utilizing deep learning techniques. We examine past consumption data, weather trends, and market variables using sophisticated neural network architectures, such as CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory). The suggested models outperform conventional forecasting techniques after being trained on a variety of datasets. The findings show that deep learning greatly lowers forecasting mistakes, allowing utilities and stakeholders to make better decisions. The results highlight how artificial intelligence may be used to optimize pricing and resource allocation for energy, which will ultimately lead to a more dependable and effective power market.