Intelligent neural network design for forecasting loads in electric micro networks
Being able to predict power demand and output from renewable energy sources is an essential asset for the optimization of the performance of electric networks. In the particular case of microgrids the importance of that ability is enhanced even more so, since in general a great percentage of the ene...
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Uruguay |
| Recursos: | Universidad de Montevideo |
| Repositorio: | REDUM |
| Idioma: | español |
| OAI Identifier: | oai:redum.um.edu.uy:20.500.12806/2526 |
| Acesso em linha: | http://revistas.um.edu.uy/index.php/ingenieria/article/view/381 |
| Access Level: | acceso abierto |
| Palavra-chave: | Redes neuronales artificiales Pronósticos Microrredes eléctricas Algoritmos genéticos Artificial Neural Networks (ANN) Forecasting Electric Microgrids Genetic Algorithms |
| Resumo: | Being able to predict power demand and output from renewable energy sources is an essential asset for the optimization of the performance of electric networks. In the particular case of microgrids the importance of that ability is enhanced even more so, since in general a great percentage of the energy generated comes from renewable sources. These parameters fluctuate substantially due to the scale in which they operate, so the need to predict their values acquires further significance. In this article we propose a methodology for the design of forecasting systems based on artificial neural networks (ANN) |
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