Por favor, use este identificador para citar o enlazar este ítem:
https://hdl.handle.net/11000/31194
Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting
Título : Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting |
Autor : López, Miguel Sans, Carlos VALERO, SERGIO Senabre, Carolina |
Editor : MDPI |
Departamento: Departamentos de la UMH::Ingeniería Mecánica y Energía |
Fecha de publicación: 2018-08-10 |
URI : https://hdl.handle.net/11000/31194 |
Resumen :
Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF)
in the last 20 years and it has partly displaced older time-series and statistical methods to a second
row. However, the STLF problem is very particular and specific to each case and, while there are
many papers about AI applications, there is little research determining which features of an STLF
system is better suited for a specific data set. In many occasions both classical and modern methods
coexist, providing combined forecasts that outperform the individual ones. This paper presents a
thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as
forecasting engines. The objective of this paper is to determine the circumstances under which each
model shows a better performance. It analyzes one of the models currently in use at the National
Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques.
The parameters that are tested are the availability of historical data, the treatment of exogenous
variables, the training frequency and the configuration of the model. The performance of each model
is measured as RMSE over a one-year period and analyzed under several factors like special days
or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions.
However, the NN model performs more accurately under certain stress situations.
|
Palabras clave/Materias: short-term load forecasting (STLF) neural networks artificial intelligence (AI) |
Área de conocimiento : CDU: Ciencias aplicadas: Ingeniería. Tecnología: Ingeniería mecánica en general. Tecnología nuclear. Electrotecnia. Maquinaria |
Tipo documento : application/pdf |
Derechos de acceso: info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI : https://doi.org/10.3390/en11082080 |
Aparece en las colecciones: Artículos Ingeniería Mecánica y Energía
|
La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.