Deep Learning Attention Mechanisms Combined with Roll Padding: Application to Household Electric Power Consumption

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Goncalves, R (Autor) Outra; Ribeiro, VM (Autor) FEP; Pereira, FL (Autor) Outra

Ata de Conferência Internacional

Título: TECHNOLOGIES, MARKETS AND POLICIES: BRINGING TOGETHER ECONOMICS AND ENGINEERING

Páginas: 249-254

5th International Conference on Energy and Environment - bringing together Economics and Engineering (ICEE)

Univ Porto, Sch Econ, Porto, PORTUGAL, JUN 02-03, 2022

Indexação

ISI Web of Knowledge - 0 Citações

Outras Informações

ID Authenticus: P-00X-64X

Abstract (EN): The accurate prediction of electric energy consumption in the residential sector is a desirable action to ensure the minimization of potential losses and the maximization of social welfare. This study proposes a new Deep Learning Neural Network architecture conceived for multivariate time series problems, which consists of including a special mechanism of attention taking the form of a multi-head bi-dimensional convolution and a novel padding method called roll padding into a ConvLSTM2D model. After being trained, tested and compared to several benchmark alternatives considering the Household Electric Power Consumption data set provided by the University of California at Irvine machine learning repository, results show that the proposed model exhibits the lowest forecasting error in the predictive exercise.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6