Assessing AI Applications of Energy Management in Manufacturing: A Case Study of Engineers' Training Course in Thailand

Abstract

The application of technology for energy management in manufacturing is a critical issue gaining increasing attention, particularly in energy consumption in industrial plants. Many studies have proposed methods to improve energy efficiency and achieve savings, yet the most advanced technologies are often complex to transfer and require specialized engineering expertise. Additionally, traditional training methods must enhance their ability to effectively bridge the gap between academic knowledge and real-world industrial applications. This study explores integrating artificial intelligence (AI) technology in energy management through training courses focused on appropriately adjusting the pressure in compressed air systems (CAS). Specifically, the Adaptive Nereo-Fuzzy Inference System (ANFIS) was used to analyze data from these systems and manage energy consumption across different operating conditions. The study involved 34 engineers who participated in these training courses. A paired samples t-test was conducted to assess changes in engineers’ understanding before and after the training, revealing a significant improvement. Overall, the engineers perceived the training as beneficial and responded positively.