Smart Metering System for Real-Time Energy Monitoring, Forecasting and Anomaly Detection

Abstract

This paper describes a fully functional smart metering system with an end-to-end flow: it collects domestic electrical parameters, stores data using MongoDB as a service, and applies machine learning for demand prediction and anomaly detection. The back end is built with Express.js and Mongoose for schema design, while the Python Flask framework hosts two microservices: one for linear regression-based one-step-ahead power forecasting and another for outlier identification using Isolation Forecast. A web interface provides form-based interaction and data visualisation. The system\'s flexible, modular architecture and provide a dashboard to extendible data models support a wide range of energy analytics applications.

Introduction

Digitalized energy management requires real-time monitoring, predictive insights, and anomaly detection, which older utility meters cannot provide. This system integrates a web-based backend with an independent machine learning (ML) service to forecast short-term energy usage and detect unusual consumption patterns. The design leverages open-source components and is modular, consisting of a frontend interface, backend server (Node.js/Express), persistent storage (MongoDB), and an ML/analytics layer (Flask) for Linear Regression forecasting and Isolation Forest anomaly detection.

The methodology involves collecting energy data, storing it securely, predicting future usage via linear regression, and detecting anomalies using Isolation Forest. Users interact through a web frontend that displays real-time and historical data, predictions, and allows feedback. Experimental validation confirmed the system’s end-to-end functionality, accurate baseline forecasting, and timely anomaly signaling.

Challenges include high smart meter costs, integration with legacy systems, large data volumes, privacy/security concerns, network reliability, and user resistance.

Future scope envisions AI, IoT, blockchain, 5G, and edge computing integration for scalable, real-time analytics, enhanced security, user-centric dashboards, support for renewable energy, and improved sustainability and energy management transparency.

Conclusion

The smart energy management system being developed will incorporate web-based monitoring capabilities, a secure back end, as well as machine learning (ML) based analytical tools to enable an integrated platform for energy use management that can meet the needs of today’s energy users. The proposed system is designed to provide utility providers and consumers with timely and useful information (real-time usage, predictive load forecasting, anomaly detection) to optimize their energy use and ensure reliable service. The system has been designed to be easily scalable to accommodate both residential and institutional applications by using a modular design, which also ensures accurate data collection and transparent operations. The development of this system is to serve as a foundation for future predictions in smart energy management systems

References

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Copyright

Copyright © 2025 Prof. Aparna Pandey, Peddu Saket, Vibhav Bansal, Mukhtar Ahmed . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.