The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification.

The efficiency of solar panels decreases as the temperature increases and heat dissipation becomes a serious problem in hot environments such as the Arabian desert. This paper investigates the use of a phase change material (PCM-OM37P) to maintain panel temperatures close to ambient. The enhancement of the GCL–P6/60265W solar panel efficiency was demonstrated at the University of Tabuk Renewable Energy and Energy Efficiency Center (REEEC). As these solar panel arrays are remotely monitored, we were able to demonstrate the validity of our cooling solution. During peak times, a drop voltage of at least 0.6V has been realized using the PCM for cooling the PV panel. This corresponds to a cooling temperature of 5 to 6°C. This difference in operating voltages between the PCM-cooled and the reference PV panels translates into a power enhancement percentage (PEP) of about 3%. The PEP value was underestimated due to the PV string configuration where the operating electrical current is taken as the average value for both PV panels.

This is a comprehensive research endeavor focused on enhancing the efficiency of the proposed solar cell design. The integration of the simulation techniques, judicious material selection, and meticulous performance metrics showcase a methodical approach toward creating a solar cell capable of achieving high efficiency across a wide spectrum of light in the AM 1.5 G1 sun solar cell illumination spectrum. Having said this, many researchers are still working on the efficiency potential—based on external radiative efficiency (ERE), open-circuit voltage loss, and fill factor loss—of high-efficiency solar cells. The solar cell is built on aluminum-doped zinc oxide (ZnO) as a transparent conductive oxide layer; aluminum nitride (AlN) as the window layer (emitter); an SWCNT layer as the absorber layer; gallium phosphide (GaP) as the contact layer; and silicon as the substrate. The proposed solar cell transmission, reflection, and absorption relative to the variations in wavelength band spectrum are studied. The conduction and valence band energy diagrams of the solar cell design structure are simulated against the layer thickness variations for the suggested solar cell structure. Short-circuit current density and maximum power variations are clarified versus the bias voltage. Light current density is simulated versus the bias voltage (J/V characteristics curve) of the suggested solar cell design structure. The carrier generation–recombination rate is also simulated by the COMSOL simulation program versus the layer thickness of the suggested solar cell structure. The solar cell circuit design has a fill factor (FF) value of 74.31% and a power conversion efficiency value of 29.91%.