Smart Energy Digital Twin: Real-Time 3D Spatial Visualization and IoT-Based Residential Energy Management

Residential electricity consumers typically learn about their consumption only when the monthly bill arrives, without knowing which room or appliance wastes the most energy. Conventional meters display energy in kilowatt-hours rather than real-time monetary cost and provide no early warning when the load approaches the circuit limit, risking miniature circuit breaker (MCB) trips. This paper presents Smart Energy Digital Twin, a system that combines an interactive 3D spatial visualization of a house built with WebGL (Three.js) with real-time Internet of Things (IoT) energy monitoring. The hardware layer uses an ESP32 microcontroller, a PZEM-004T AC power sensor, and relay modules; measurements are synchronized between the physical house and its virtual replica through the Firebase Realtime Database. A rule-based expert system converts instantaneous power into Indonesian Rupiah using dynamic PLN tariffs ranging from 450 VA to 5500 VA and raises early warnings for overload conditions and wasteful usage patterns. The prototype implements spatial 3D interaction with smooth gliding camera transitions, a live power chart, an AI alert box, and two-way IoT control for switching appliances on and off. Functional testing confirms that the three heuristic rules correctly trigger overload alarms, wasteful-pattern advisories, and eco-mode power reduction. The tariff conversion covers seven residential classes from 450 VA to 5500 VA, and the 3D visualization embeds every measurement in its physical room location so users immediately see where energy is consumed. The main contribution is the integration of spatial 3D visualization, financial monitoring, and heuristic AI alerts into a single residential digital twin, a combination rarely addressed in previous studies that focus on two-dimensional dashboards or industrial-scale twins.