Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset
This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the appli...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2015
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/59405 http://44jaiio.sadio.org.ar/sites/default/files/sio57-66.pdf |
| Aporte de: |
| id |
I19-R120-10915-59405 |
|---|---|
| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Brasil Redes Neurales (Computación) Algoritmos |
| spellingShingle |
Ciencias Informáticas Brasil Redes Neurales (Computación) Algoritmos Defilippo, Samuel B. Neto, Guilherme G. Hippert, Henrique S. Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| topic_facet |
Ciencias Informáticas Brasil Redes Neurales (Computación) Algoritmos |
| description |
This paper studies the application of genetic algorithms in helping to select the proper architecture and training parameters, by means of evolutionary simulations done on a series of real load data, for a neural network to be used in electric load forecasting. Particularly, we investigate the application of a novel fitness function to the genetic algorithms, instead of the usual ones, based on the sum of the squares of the errors. We compare the results of the neural networks thus specified with that of four benchmarks: two naive forecasters, a linear method, and a neural network in which the parameter values are found by means of a grid search. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Defilippo, Samuel B. Neto, Guilherme G. Hippert, Henrique S. |
| author_facet |
Defilippo, Samuel B. Neto, Guilherme G. Hippert, Henrique S. |
| author_sort |
Defilippo, Samuel B. |
| title |
Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| title_short |
Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| title_full |
Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| title_fullStr |
Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| title_full_unstemmed |
Short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a Brazilian dataset |
| title_sort |
short-term load forecasting by artificial neural networks specified by genetic algorithms – a simulation study over a brazilian dataset |
| publishDate |
2015 |
| url |
http://sedici.unlp.edu.ar/handle/10915/59405 http://44jaiio.sadio.org.ar/sites/default/files/sio57-66.pdf |
| work_keys_str_mv |
AT defilipposamuelb shorttermloadforecastingbyartificialneuralnetworksspecifiedbygeneticalgorithmsasimulationstudyoverabraziliandataset AT netoguilhermeg shorttermloadforecastingbyartificialneuralnetworksspecifiedbygeneticalgorithmsasimulationstudyoverabraziliandataset AT hipperthenriques shorttermloadforecastingbyartificialneuralnetworksspecifiedbygeneticalgorithmsasimulationstudyoverabraziliandataset |
| bdutipo_str |
Repositorios |
| _version_ |
1764820478480875523 |