Evaluation of classification methods according to solar radiation features from the viewpoint of the production of parabolic trough CSP plants
In this work, the representativeness of the day-types classified according to the solar radiation features by two classification methods is evaluated from the perspective of the production of two parabolic trough plants. A new methodology to characterize the representativeness of the day-types using...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/163039 |
| Acceso en línea: | https://hdl.handle.net/11441/163039 https://doi.org/10.1016/j.renene.2018.01.040 |
| Access Level: | acceso abierto |
| Palabra clave: | Solar radiation Parabolic trough Concentrated solar power (CSP) plant System advisory model (SAM) Sky conditions Day type |
| Sumario: | In this work, the representativeness of the day-types classified according to the solar radiation features by two classification methods is evaluated from the perspective of the production of two parabolic trough plants. A new methodology to characterize the representativeness of the day-types using a novel index is proposed, based on the characterization of the daily production. As a previous step to the use of a classification method, the evaluation methodology helps to select the most adequate model and to improve it from the perspective of a concentrated solar power project. This methodology is applied to 16 years of measurements from Seville (Spain) classified by two methods: a method based on daily clearness index values (kt), and a method that uses clustering techniques to define the day-types. From the application of the methodology to the clustering classification some improvements are identified and applied. As a result, from the 10 day-types identified by the clustering classification method a new classification based on 8 day-types with different features for each type of plant is proposed. The use of this classification to estimate the daily yield outperforms the results obtained with the kt classification, with a mean yearly RMSE value more than 20% lower. |
|---|