This paper describes the development of a deep neural network architecture based on
transformer encoder blocks and Time2Vec layers for the prediction of electricity prices …

Forecasting electricity prices is a key tool for power market participants in making market
decisions which has received an increased importance with growing renewable market …

Redundant complexities and inadequate representation of spatiotemporal features are
included in the electricity price data. To address complex data redundancy and inadequate …

This praxis develops a predictive model for forecasting Locational Marginal Prices (LMP),
considering their time-varying and locational characteristics to deliver valuable price insights …

The increased use of renewable energy sources has brought numerous environmental
benefits. However, a significant challenge with renewable energy is the inability to control …

Este trabalho tem como objetivo principal a predição do Preço de Liquidação das
Diferenças (PLD) para o submercado sudeste, em um intervalo crescente de 1 a 7 dias, por …