Solar energy is the most conventional energy that can be used in the real world. It can be used from small application to a large level of Industry application. It is the secondary scope in the field of electricity generation. Identifying the faults in the solar system can be used to increase the productivity of the application wherever using the system. Deep learning based solar power fault identification can be used as a current technology to increase the expected output performance of the solar power plant. In addition, with identifying the faults in physical mode for a large area is difficult and it can be identified by the deep learning model for improving the stability of the system. Deep learning (DL) method is used to examine the number of reasons for reducing the power generation of solar system. Power production for each panel is compared with the neighbor panel to identify the faults easily. It can be done by I-V comparison of each and every cell can be done and compared using the Deep Learning techniques. Thermoelectric generator type can also be used where hot spot area and shading area. It can be implemented for obtaining maximum power from the system. Combining the I-V comparison method on each neighbor panel and the thermoelectric generator method in shaded area and hot spot area induces high efficiency in the solar panel with large area power production.