International Journal of Technology and Applied Science

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SMARTWATT NEXUS

Author(s) T. Ramya Sri, Md. Asma Begum, P. Vasavi, B. Jayasree, B. Vaishnavi
Country India
Abstract Electricity consumers and utility operators increasingly require systems that move beyond retrospective billing and provide timely, actionable intelligence. This paper presents SMARTWATT NEXUS, an AIoT-based platform that integrates real-time electricity data ingestion, anomaly-aware analytics, and short-horizon consumption forecasting within a single deployment-oriented architecture. The platform supports dual ingestion paths: secure IoT API uploads and Zigbee2MQTT stream subscriptions. To handle field heterogeneity, the backend normalizes multiple payload formats, including cumulative energy values and power-only readings, with automatic conversion of instantaneous power samples to energy estimates. Forecasting is performed using a hybrid model stack that includes Long Short-Term Memory (LSTM), a regression ensemble based on Linear Regression and Random Forest Regressor, and an Artificial Neural Network (ANN). The system also includes automated meter-to-user association, health observability endpoints, dashboard analytics, and engagement mechanisms such as badges and recognition workflow. Implementation-level evaluation demonstrates stable ingestion behavior, successful handling of mixed telemetry schemas, reliable alert generation for abnormal usage, and continuous forecast availability after minimal historical data accumulation. The proposed framework offers a practical, extensible baseline for intelligent residential and institutional energy management.
Keywords AIoT, smart energy, electricity forecasting, Zigbee2MQTT, anomaly detection, LSTM, ANN, IoT analytics.
Field Engineering
Published In Volume 17, Issue 4, April 2026
Published On 2026-04-12
Cite This SMARTWATT NEXUS - T. Ramya Sri, Md. Asma Begum, P. Vasavi, B. Jayasree, B. Vaishnavi - IJTAS Volume 17, Issue 4, April 2026.
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