Home Energy Use Optimizer: A Review of IoT, AI, and Fault-Aware Residential Energy Management

Abstract

Residential electrification, connected appliances, and variable tariffs have increased the need for practical home energy management systems (HEMS). Existing research demonstrates the value of optimization, Internet of Things (IoT) monitoring, artificial intelligence (AI), machine learning (ML), and model predictive control for reducing electricity cost and peak demand. However, many solutions emphasize scheduling and forecasting while treating appliance-level fault detection and automatic safety isolation as secondary functions. This paper presents a structured narrative review of 13 studies published between 2020 and 2026 and synthesizes their objectives, methods, strengths, and implementation gaps. Based on the synthesis, a low-cost Home Energy Use Optimizer is proposed using an ESP32/Arduino-class controller, voltage and current sensing, relay-based load isolation, local display, Wi-Fi connectivity, bill estimation, and user alerts. The architecture combines real-time monitoring, threshold-based protection, remote supervision, and extensibility toward predictive analytics. The paper identifies hardware validation, interoperable device integration, cybersecurity, explainable control, and field evaluation as priorities for future work.