Electricity demand forecasting using a sarimamultiplicative single neuron hybrid model
The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of...
- Autores:
-
Velásquez Henao, Juan David
Rueda Mejía, Viviana María
Franco Cardona, Carlos Jaime
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/73049
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/73049
http://bdigital.unal.edu.co/37524/
- Palabra clave:
- energy demand
energy markets
nonlinear models
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron models. |
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