A Survey on IoT and Artificial Intelligence-Based Smart Home Energy Management Systems: Techniques, Challenges and Future Directions

Abstract: The increasing penetration of Internet of Things (IoT) devices, renewable energy resources, smart meters, and connected appliances has created new opportunities for intelligent energy management in residential environments. Conventional energy management approaches are often limited by manual monitoring, inaccurate demand estimation, limited interoperability, and the inability to respond dynamically to changing consumption patterns. This paper presents a technical survey of recent research in IoT- and Artificial Intelligence (AI)-based smart home and smart grid energy management. Twenty representative studies covering energy monitoring, load forecasting, non-intrusive load monitoring, demand response, appliance scheduling, anomaly detection, optimization, edge AI, and user behavior modeling are analyzed. The reviewed studies demonstrate that machine learning, deep learning, optimization algorithms, IoT communication technologies, and edge computing can substantially improve energy awareness, forecasting, cost reduction, and automated control. However, significant challenges remain in dataset availability, cross-household generalization, computational complexity, cybersecurity, privacy, interoperability, and large-scale deployment. The survey develops a taxonomy of existing approaches, compares their methodologies and reported results, identifies research gaps, and discusses future directions toward adaptive, secure, low-cost, explainable, and autonomous home energy management systems.

Keywords: Internet of Things, smart home, energy management, artificial intelligence, load forecasting, demand response