The utilization of smart technologies based on the Internet of Things (IoT) and Machine Learning (ML) has emerged as a crucial strategy for enhancing energy efficiency, particularly in electricity consumption monitoring and management systems. This article presents a Systematic Literature Review (SLR) of scholarly publications discussing the integration of IoT and ML in energy management. The review was conducted using the PRISMA framework, encompassing identification, selection, and analysis of 26 articles published between 2019 and 2024 across various academic databases. The findings indicate that integrating IoT with current sensors, motion sensors, and cloud-based processing, combined with ML algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Long Short-Term Memory (LSTM), can improve energy efficiency by up to 30% and enable real-time anomaly detection in electricity consumption. Furthermore, such systems facilitate the implementation of automated notifications as early warnings for users. However, most existing studies remain limited to providing information through dashboards that must be monitored manually. In contrast, personalized notifications delivered via instant messaging applications (e.g., WhatsApp, Telegram) or dedicated mobile apps have the potential to engage users more effectively in energy management. Nevertheless, challenges persist in terms of installation costs, data security issues, as well as limitations in infrastructure and human resources. These findings underscore the importance of developing IoT–ML–based energy management technologies that are not only efficient but also sustainable, thereby supporting energy-saving policies and environmental sustainability in the future.
Suwarjono et al. (Wed,) studied this question.