An international group of scientists developed a novel dust detection method for PV systems. The new technique is based on deep learning and utilizes an improved version of the adaptive moment estimation (Adam) optimization algorithm, which is commonly used to train networks, to provide pre-planning for solar panel cleaning and route on solar panel cleaning robots for automatically cleaning.
In order to overcome those issues, the academics proposed integrating Warmup and cosine annealing strategies into the algorithm. The Warmup technique uses a small learning rate in the early stages of training, gradually increasing it. "This helps the model to better explore the parameter space in the early stages of training, avoiding oscillation or divergence problems caused by excessive learning rate," they added.
The cosine annealing strategy, on the other hand, causes the learning rate to change periodically between the maximum and minimum learning rates, according to the annealing curve of the cosine function. "This strategy helps to prevent the oscillation problem caused by the rapid descent speed of the gradient, thereby improving the training stability and generalization performance of the model," the researchers explained.
