Very short-term parametric ambient temperature confidence interval forecasting to compute key control parameters for photovoltaic generators
In recent years, various forecasters have been developed to decrease the uncertainty related to the intermittent nature of photovoltaic generation. While the vast majority of these forecasters are usually just focused on deterministic or probabilistic prediction points, few studies have been carried...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Navarra |
| Repositorio: | Dadun. Depósito Académico Digital de la Universidad de Navarra |
| Idioma: | inglés |
| OAI Identifier: | oai:dadun.unav.edu:10171/63763 |
| Acceso en línea: | https://hdl.handle.net/10171/63763 |
| Access Level: | acceso abierto |
| Palabra clave: | Confidence interval forecast Very short-term horizon Temperature Smart control Photovoltaic generation |
| Sumario: | In recent years, various forecasters have been developed to decrease the uncertainty related to the intermittent nature of photovoltaic generation. While the vast majority of these forecasters are usually just focused on deterministic or probabilistic prediction points, few studies have been carried out in relation to prediction intervals. In increasing the reliability of photovoltaic generators, being able to set a confidence level is as important as the forecaster’s accuracy. For instance, changes in ambient temperature or solar irradiation produce variations in photovoltaic generators’ output power as well as in control parameters such as cell temperature and open voltage circuit. Therefore, the aim of this paper is to develop a new mathematical model to quantify the confidence interval of ambient temperature in the next 10 min. Several error metrics, such as the prediction interval coverage percentage, the Winkler score and the Skill score, are calculated for 95%, 90% and 85% confidence levels to analyse the reliability of the developed model. In all cases, the prediction interval coverage percentage is higher than the selected confidence interval, which means that the estimation model is valid for practical photovoltaic applications. |
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