Electricity consumption forecasting using singular spectrum analysis
Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models wh...
- Autores:
-
Menezes, Moises Lima de
Castro Souza, Reinaldo
Moreira Pessanha, José Francisco
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/60744
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/60744
http://bdigital.unal.edu.co/59076/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Electricity consumption forecasting
singular spectrum analysis
time series
power system planning
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil. |
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