Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Bhavana Pabbuleti
SRK Institute of Technology
Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Empowering energy management: anomaly detection in smart meter data for proactive consumption control Batchalakuri Jyothi; Bhavana Pabbuleti; Beeravalli Mounika; Hrushitha Kalapala; Meda Uma Santhosh Chandra; Sanaboina Sai Srilakshmi; Bommasani Ganesh Babu; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Rami Reddy Chilakala
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10957

Abstract

The increasing deployment of smart energy meters (SEMs) has enabled real-time monitoring of energy consumption, but the vast data generated makes it challenging to detect anomalies that may indicate inefficiencies, faults, or unauthorized usage. This study aims to enhance energy management by developing a hybrid anomaly detection framework that improves accuracy while providing actionable insights for consumers. The proposed method integrates statistical and machine learning (ML) approaches, specifically Z-score, local outlier factor (LOF), one-class support vector machine (SVM), and isolation forest (iForest), to analyze simulated smart meter data. An anomaly is flagged only when identified by all four methods, thereby reducing false positives and improving reliability. The framework is implemented in an interactive dashboard built with streamlit, offering real-time visualization, peak-time alerts, usage forecasts, and personalized consumption suggestions. Results demonstrate that the hybrid approach outperforms single-method models, achieving higher detection accuracy and practical applicability. The findings highlight the potential of combining complementary detection techniques with proactive feedback to empower consumers, reduce energy wastage, and support sustainable energy management. This work provides a scalable foundation for future real-time deployment in smart grids and microgrid environments.
Smart internet of things driven vehicle safety and battery management system Batchalakuri Jyothi; Bhavana Pabbuleti; Ravi Ponnala; Patil Mounika; Edara Shiva Prasad; Enukonda Soumya; Rakesh Teerdala; Srungaram Ravi Teja; Malligunta Kiran Kumar; Kambhampati Venkata Govardhan Rao; Chilakala Rami Reddy
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1085-1097

Abstract

The proposed system design, the essential results obtained, and its relevance to modern automotive applications. The updated version now begins by identifying the dual challenge of increasing road accidents and the performance degradation of electric vehicle (EV) batteries, emphasizing the lack of integrated solutions that address both safety and battery reliability in a unified manner. Then, this study concisely describes the system design, highlighting the use of an internet of things (IoT)-enabled architecture incorporating an ADXL345 accelerometer for collision detection, global positioning system (GPS)/global system for mobile communications (GSM) modules for emergency alerting, and voltage, current, and temperature sensors for real-time battery monitoring, all interconnected through Arduino and ESP8266-based cloud communication. The abstract now summarizes key experimental results, including the system’s demonstrated accuracy in impact recognition, reduced alert latency, and reliable battery-parameter tracking supported by effective cloud visualization. Finally, this study strengthened the automotive relevance by emphasizing how this integrated platform enhances emergency responsiveness, supports predictive maintenance, and offers practical value for EVs, commercial fleet operations, and broader intelligent transportation applications. The revised abstract now provides a balanced, complete, and technically informative overview aligned with academic and automotive industry expectations.
Co-Authors Batchalakuri Jyothi Beeravalli Mounika Bommasani Ganesh Babu Chilakala Rami Reddy Edara Shiva Prasad Enukonda Soumya Hrushitha Kalapala Kambhampati Venkata Govardhan Rao Malligunta Kiran Kumar Meda Uma Santhosh Chandra Patil Mounika Rakesh Teerdala Rami Reddy Chilakala Ravi Ponnala Sanaboina Sai Srilakshmi Srungaram Ravi Teja