Explainable AI and Industrial Internet of Things (IIoT)-Based Energy-Saving Design: Application for the Electrical Industry

The ever-increasing need for energy has caused the field of industrial electrical engineering to place a greater emphasis on designs that save energy. This is done with the intention of lowering carbon emissions as well as overall consumption. Transformers, which are essential parts of power supply and distribution systems, may be designed with an energy-efficient layout, which is one of the significant aspects of this phenomenon. This study makes use of Explainable AI (XAI) to investigate power loss in transformers, describe technical factors, determine the number of transformers, and optimize economic functioning using XAI algorithms. In order to attain superior results than those obtained via the use of conventional methods, the energy-saving design approach that was created in this research makes use of IoT sensors and XAI. Experiments that were carried out on a five-star hotel in the province of Jilin revealed a 9.8 and 43.5% decrease in complete power loss for two substations, respectively, when compared to the original design. This resulted in considerable cost reductions in yearly and entire life loss energy expenses. The research provides conclusive evidence that XAI is an efficient tool for generating cost savings and energy efficiency for industrial electrical applications.