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Journal of Power Electronics and Power Systems Review Article

ENERGY CONSUMPTION MONITORING WITH A MOBILE APPLICATION

  1. S. Ravi kumar Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology
  2. M.Antony Sharon Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology
  3. D. Bebin Philipson Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology
  4. P. Berlin Meshak Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology
  5. S. Sivanth Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology

Abstract

Energy consumption monitoring via mobile applications has emerged as a crucial paradigm in modern smart grids and Internet of Things (IoT) frameworks, enabling real-time visibility and proactive energy management for end consumers. By bridging hardware sensors, wireless communication protocols, cloud analytics, and interactive mobile interfaces, these systems transform raw electrical telemetry into actionable insights. This technology addresses critical challenges such as peak demand management, energy inefficiency, phantom power loss, and user behavioral awareness across residential and commercial settings. The primary purpose of this literature survey is to provide a comprehensive analysis of 20 foundational peer-reviewed studies detailing system architectures, low-power mobile optimizations, Non-Intrusive Load Monitoring (NILM), machine learning predictions, edge computing offloading, and user experience (UX) dynamics driving contemporary energy monitoring solutions. Moreover, the survey analyzes the smart meter, low-cost microcontrollers, and lightweight communication protocols to further enhance the monitoring accuracy, decrease the data transmission latency, and facilitate the efficient analysis of electricity usage. It also points to the significance of mobile visualization, automatic notifying and cost estimation of consumption in providing informed decisions by users. The results indicate several long-term concerns: battery life of the devices, delays in network connections, sensor calibration, data privacy, and ongoing user engagement. Future research avenues involve appliance-level load identification, privacy-preserving analytics and more efficient edge computing architectures for scalable and sustainable energy management.

Keywords

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