In this project, a residential-scale solar desalination system is investigated. The design includes evacuated-tube solar heaters with a heat pipe, and flashing units comprising three desalination stages. The experiments included single-stage, double-stage, and triple-stage desalination. The results showed that production rates of 2.2 kg/m2 using single-stage, 4.7 kg/m2 using double-stage, and 6.4 kg/m2 using triple-stage desalination were obtained from the system. The solar water heater used in the system exhibited an average thermal efficiency of 73 %. The economic analysis revealed that the payback period for the triple-stage desalination system is 8 years. The annualized rate of return was calculated to be 2.2 %, assuming the system operates for 4 h per day and 300 days per year. 

In this project, a shading anomaly detection framework comprised three stages: An autoencoder-convolutional neural networks CNN model, a mean absolute error MAE threshold, and data filters. The framework was developed to detect the occurrence and location of partial shading on bi-facial modules. Several experiments were carried out using two bi-facial modules under different shading settings. The modules were connected to solar chargers and batteries to analyze their performances. The experimental results showed the modules’ generated current and the batteries’ state of charge SOC in all shading settings. The results also showed that anomalies or shading can be detected with an accuracy of more than 99% merely from the second stage of the framework. However, the location of shading can be classified and predicted with an accuracy of 91% by utilizing all three stages of the framework. 

The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.