A Cost Effective IOT Solutions for Energy Usage Management

Abstract

Thepaperfocusonthedevelopmentofan‘ACostEffectiveIOTSolutionsforEnergyUsageManagement ’. With the growing demand of electricity its somewhere hard to manage the efficient energy. Traditional Meterswhichisfoundineverywhere nowadays requiremanualreadingandoftenleadtoerrorsanddelayin billing.Asmartenergyaddressedtheseissuesbyenablingreal-timemonitoring and automated-billing. Bythe use of smart energy meter, it allow users to track their energy and get updated all time through an app or a web dashboard. The smart meter is recharged with the help of Esp module. In smart energy meter, energy utilizationand thecorrespondingamountwillbedisplayedonthe LCDasaremainder. Thefeedbackfromtheuserhelp sinidentifyingtheusagesbetweenauthorizedandunauthorizeduserswhichhelpsincontrollingthe power wastage. Esp module is used for sending messages to the local authorities regarding user power consumptions. Also they canmonitor themeterreadingsregularly withoutmakingeffor tstovisiteachhouse fortakingmanualreadings. Thistechnologynotonlyreducethehuman effortsbutalsopromotesenergyconservationby allowing andencouraginguserstomonitorandoptimizetheir energyconsumption.

Introduction

Electricity is vital in daily life, but traditional meters requiring manual readings often cause errors and delays. Smart energy meters, using advanced IoT technology and sensors like LDR and microcontrollers (Arduino and ESP12E NodeMCU), enable real-time, accurate monitoring and automatic data transmission to cloud systems. This modern approach reduces human effort, improves billing accuracy, detects faults, and prevents power theft.

Smart meters provide users with detailed consumption data via mobile or web apps, enabling energy conservation, cost savings, and environmental benefits. They support features like automatic billing, load management, fault alerts, dynamic pricing, and integration with smart homes and grids.

The technology uses low-power devices, secure data transmission protocols, and can predict maintenance needs through data analytics. It promotes efficient energy use in residential, commercial, and industrial settings while fostering consumer engagement in sustainable energy practices.

The literature review highlights various IoT-based smart meter solutions focusing on real-time tracking, cloud integration, wireless monitoring, big data analytics, and smart grid applications.

Conclusion

“ACostEffectiveIOTSolutionsfor EnergyManagement”,representasignificanttechnologicaladvancementin energy management, offering real-time monitoring, automated billing, and enhanced control over energy consumption. This energy management offer a smart and affordable way to save energy and reduce energy cost. By using connected sensors and smart meters or automations ,businesses and homes can track how much electricitytheyuseondailybasisandavoidenergywastage. Byintegratingwithsmartgrids, artificialintelligence, and data analytics, these meters empower consumers and utilities to make informed decisions, optimize energy usage, and reduce operational costs. This solutions not only help lower power bills but also support greener and moresustainablefuture.WiththefallingcostofIOTdevices andeasyaccesstocloudplatform,anditsnowmore practical then ever to adopt these technologieswithout spending too much. While challenges such as high initial costs, data security concerns, and infrastructure requirements exist, ongoing technological advancements and widespread adoption are expected to overcome these hurdles. Overall, IoT-based smart energy meters have the potentialtorevolutionizeenergy management,contributingto greaterefficiency,sustainability, andthefutureof smart cities. Overall, the IOT is providing to be a powerful and cost-effective tool for managing energy smartly and efficiently. Furtherresearchcanbeconductedtoimprovethescalabilityofthesystem,makingitadaptableforusenotjust in homes but in large industrial and commercial environments. Integration with smart home devices and IoT- based appliances could automate power management further, allowing the system to control energy consumption intelligently based on user behavior and preferences.

References

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Copyright

Copyright © 2025 Prof. Prachiti Adghulkar, Shreya P. Bangde, Khushi A. Kokode, Khushi G. Urade, Vaishnavi R. Kharwade, Sonam K. Bhoriya. 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.