Design of a user electricity consumption reminder system and sensitivity analysis of credit ratings based on big data analytics

With the continuous growth of electricity demand, the efficient management of energy consumption has become a critical issue. However, many users lack awareness of their electricity usage patterns, leading to inefficient consumption behaviors and financial burdens. This paper proposes a novel user electricity consumption reminder system integrated with big data analytics to address these challenges. The proposed system analyzes user electricity consumption patterns using clustering algorithms and machine learning models, providing personalized consumption reminders. Additionally, a sensitivity analysis of credit ratings is conducted to explore the impact of electricity payment behavior on user credit. Experimental results demonstrate the system’s effectiveness in enhancing user awareness, promoting sustainable energy practices, and maintaining financial stability. The study highlights the advantages of integrating big data techniques into user-centered energy management systems and suggests directions for future research.