A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/22425 |
| Acceso en línea: | https://hdl.handle.net/2117/22425 https://dx.doi.org/10.4316/AECE.2014.01002 |
| Access Level: | acceso abierto |
| Palabra clave: | Genetic algorithms Adaptive neuro-fuzzy inference system Energy forecast Genetic algorithm Intelligent energy management systems Programació genètica (Informàtica) Energia -- Gestió Àrees temàtiques de la UPC::Enginyeria electrònica |
| Sumario: | This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes. |
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