Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation an...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/196334 |
| Acceso en línea: | http://hdl.handle.net/11336/196334 |
| Access Level: | acceso abierto |
| Palabra clave: | ARTIFICIAL INTELLIGENCE GENERATION PREDICTION MEASUREMENTS PHOTOVOLTAIC ENERGY https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation and climatic parameters. For this purpose, it is needed a good sensing and measurement of these parameters. In this paper, we propose a method based on artificial intelligence techniques for obtaining the generated energy under climatic conditions during a year. In addition, we propose a model that relates short-circuit current with radiation, considering the true nonlinear behavior of the relationship between variables. The results of the proposed method using real data show its validity and usefulness in predicting the generated energy by photovoltaic modules and the search for alternative methods of measuring global radiation at low cost and reasonable error. |
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