Hierarchical time series forecast in electrical grids

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Almeida, V (Autor) Outra; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Lecture Notes in Electrical Engineering

Páginas: 995-1005

International Conference on Information Science and Applications, ICISA 2016

15 February 2016 through 18 February 2016

Indexação

Scopus - 6 Citações

Outras Informações

ID Authenticus: P-00K-7EY

DOI: 10.1007/978-981-10-0557-2_95

Abstract (EN): Hierarchical time series is a first order of importance topic. Effectively, there are several applications where time series can be naturally disaggregated in a hierarchical structure using attributes such as geographical location, product type, etc. Power networks face interesting problems related to its transition to computer-aided grids. Data can be naturally disaggregated in a hierarchical structure, and there is the possibility to look for both single and aggregated points along the grid. Along this work, we applied different hierarchical forecasting methods to them. Three different approaches are compared, two common approaches, bottom-up approach, top-down approach and another one based on the hierarchical structure of data, the optimal regression combination. The evaluation considers short-term forecasting (24-h ahead). Additionally,we discussed the importance associated to the correlation degree among series to improve forecasting accuracy. Our results demonstrated that the hierarchical approach outperforms bottom-up approach at intermediate/high levels. At lower levels, it presents a superior performance in less homogeneous substations, i. e. for the substations linked to different type of customers. Additionally, its performance is comparable to the top-down approach at top levels. This approach revealed to be an interesting tool for hierarchical data analysis. It allows to achieve a good performance at top levels as the top-down approach and at same time it allows to capture series dynamics at bottom levels as the bottom-up. © Springer Science+Business Media Singapore 2016.

Idioma: Inglês

Tipo (Avaliação Docente): Científica