Hierarchical time series forecast in electrical grids

Gomes de Almeida, Vania and Ribeiro, Rita and Gama, João (2016) Hierarchical time series forecast in electrical grids. In: 7th International Conference on Information Science and Applications (ICISA) 2016, 15-18 Feb 2016, Ho Chi Minh City.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Natural Sciences
Item ID: 23732
Useful Links:
Depositing User: Vania Gomes De almeida
Date Deposited: 05 Mar 2018 17:30
Last Modified: 05 Mar 2018 17:30
URI: http://eprints.mdx.ac.uk/id/eprint/23732

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