Binary shading classification by means of an identification algorithm for radiation transitions using low cost sensors

Abstract

In the current energy scenario, renewable energies such as solar and wind power have significant relevance. In this context, an algorithm capable of recognizing irradiation transitions was implemented. This algorithm was used to classify data obtained from light dependent resistance in two classes: shadow and non-shadow. These results were compared with those obtained by a standard algorithm that has a input global or beam irradiation values collected by pyranometer and pyreliometer sensors, respectively. This way, a practical tool was obtained. Such tool uses data collected by low cost sensors to perform an initial statistical study about days and hours when there is a higher level of irradiation. Therefore, this information can be used to optimize the solar power capture. The irradiation transition identification algorithm, generally, had a better compilance at peaks hours, where for the same days it reached ranging around 100% and 87.10% using the global and beam components as input to the standard algorithm, respectively. From the results, it is clears that a better performance of the irradiation transition identification algorithm when using global irradiation values as input to the standard algorithm, as well as a lower compilance of the irradiation transition algorithm during the dawn and twilight periods.