Deformed Ternary Phosphides III‑P for Efficient Light Control in Optoelectronic Applications

Abstract

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction. In this context, this study proposes the implementation of artificial neural networks as deep learning and the XGBoost algorithm as a machine learning method for modeling the hourly global solar radiation for a humid climate such as the Rabat region. For this purpose, hourly meteorological data from the city of Rabat in Morocco are chosen in order of importance using the random forests method, for training and testing the models, namely date and time, sunshine duration, temperature, relative humidity, wind speed/direction and pressure. Subsequently, models are selected after the validation phase for testing, whose performance is evaluated using relevant statistical indicators. As a result, we retain 2 ANN and 1 XGBoost models which are eventually very close in terms of performance with a coefficient of determination value equal to 98% and 97% respectively. However, statistical indicators have proven to be effective in assessing the accuracy and fidelity of each model. Ultimately, the intent of the modeling in terms of accuracy, simplicity or fidelity is a crucial factor in the selection of the model algorithm to adopt.