Easing Predictors Selection in Electricity Price Forecasting with Deep Learning Techniques

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Silva, AR (Autor) Outra; José Nuno Fidalgo (Autor) FEUP; Andrade, JR (Autor) Outra

Ata de Conferência Internacional

Título: European 23 International Conference on European Energy Markets

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00Y-SWX

DOI: 10.1109/eem58374.2023.10161909

Abstract (EN): This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6