In this work, an updated review of solar wood drying is presented. Experimental investigations, numerical studies and recent patents on the solar wood drying process were reviewed. The review outlines the different techniques for solar drying of wood with or without the use of thermal energy storage materials. From the review study, about 63% of published works are research papers and only a few review papers were available. A review of the numerical study aspect showed that most models were based on the simplified lumped modeling approach where several physical phenomena were ignored. However, the complex aspect of wood drying should be studied and could be modeled successfully using a new approach such as a multi-physical modeling method instead of a simplified lumped modeling approach. Therefore, more experimental and numerical studies are needed and should also consider the environmental impact and the economic viability of these solar systems.

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values. The artificial neural network model with five hidden layers obtained better results with a R² value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure.