Development of a Real-Time ANN Algorithm Based Performance Management Strategy for Energy Generation System in the Context of Energy 4.0

Abstract

In the industrial sector, ensuring optimal facility operation has become a key priority, which requires energy monitoring and control. With the integration of new technologies, optimizing energy consumption is increasingly possible, offering industries the opportunity to improve their overall performance while reducing their environmental impact. Several solutions have been presented in the literature that addresses these issues, including smart energy management systems that allow remote monitoring and control of an installation's energy consumption by IoT systems. The data collected by these systems, coupled with data analysis and artificial intelligence, offer new perspectives for optimizing energy consumption. Our aim is to propose a methodology for companies seeking to transition to the Energy 4.0 era by providing specialized digitalization solutions. Our solution is mainly based on the implementation of an ecosystem including 4.0 technologies such as artificial intelligence, the Internet of Things, and Big Data. This facilitates the transition to a new perspective of energy consumption optimization and performance improvement. To validate the applicability of our methodology, an industrial company was the subject of a case study on its boiler system, which allowed us to put our solution into practice and measure its effectiveness. The suggested method proved its effectiveness by reaching a success rate close to 99%, leading to a considerable improvement in energy consumption reduction and boiler efficiency increase.