Solar and Wind Feasibility Technologies (SWIFT)

by Manuel García-Fuente , David González-Peña and Cristina Alonso-Tristán

https://doi.org/10.3390/app12073640

The melting process of lauric acid in a square container heated from the top surface was numerically studied from an experimental case. Knowledge of this process is of special interest for computationally efficient modeling systems, such as PCM-enhanced photovoltaic panels in horizontal positions or energy storage using PCM embedded on flat surfaces. In these systems, the geometric arrangement of the PCM hinders the fluid-phase movements through natural convection, which slows the melting process and can cause overheating in the fluid phase. Using Ansys Fluent Software, three different approaches and two simulation methods, enthalpy-porosity and effective heat capacity, were developed for the numerical study. The results were compared with experimental measurements in a successful evaluation of the accuracy of computational fluid dynamics simulations. It could be observed that the effective heat capacity method presented significant advantages over the enthalpy-porosity method, since similar accuracy results were obtained, and a lower computational cost was required.

by Ana García-Rodríguez , Sol García-Rodríguez, Diego Granados-López, Montserrat Díez-Mediavilla and Cristina Alonso-Tristán

https://doi.org/10.3390/app12052372

Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type

by D. Granados-López , A. García-Rodríguez , S. García-Rodríguez , A. Suárez-García , M. Díez-Mediavilla , and C. Alonso-Tristán

https://doi.org/10.1155/2021/2636157

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.

by A.García-Rodríguez, D.Granados-López, S.García-Rodríguez, M.Díez-Mediavilla and C.Alonso-Tristán

https://doi.org/10.1016/j.agrformet.2021.108627

In this study, ten-minute meteorological data-sets recorded at Burgos, Spain, are used to develop models of Photosynthetic Active Radiation (PAR) following two different procedures: multilinear regression and Artificial Neural Networks. Ten Meteorological Indices (MIs) are chosen as inputs to the models: clearness index (kt ), diffuse fraction (kd), direct fraction (kb ), Perez’s clear sky index (ε), brightness index (Δ), cloud cover (CC), air temperature (T), pressure (P), solar azimuth cosine (cosZ), and horizontal global irradiation (RaGH). The experimental data are clustered according to the sky conditions, following the CIE standard sky classification. A previous feature selection procedure established the most adequate MIs for modelling PAR in clear, partial and overcast sky conditions. RaGH was the common MI used by all models and for all sky conditions. Additional variables were also included: the geometrical parameter, cosZ, and three variables related to the sky conditions, kt , ε, and Δ. Both modelling methods, multilinear regression and ANN, yielded very high determination coefficients (R2 ) with very close results in the models for each of the different sky conditions. Slight improvements can be observed in the ANN models. The results underline the equivalence of multilinear regression models and ANN models of PAR following previous feature selection procedures.