Exploring prepaid energy estimation, hybrid utility analytics and intelligent anomaly screening
Modern electricity distribution systems generate substantial amounts of operational and consumer data. Converting these records into useful engineering insights can support better demand assessment, energy accounting and data-driven decision-making.
At IIMEEE, my research explores practical analytical approaches for improving electricity-distribution intelligence, particularly in systems where advanced metering infrastructure and continuous monitoring are limited.
1. Estimating Energy Consumption from Prepaid Recharge Data
Prepaid electricity recharge records indicate the energy purchased by consumers, but they do not necessarily represent the energy consumed during the same month.
This research investigates a recharge-based energy estimation framework that considers historical consumption patterns, seasonal behavior and unused energy balance carried forward between months.
Research objective: Develop a transparent approach for estimating monthly prepaid energy consumption from historical recharge records.
Potential applications: Demand profiling, energy accounting and consumer consumption analysis.
2. Integrated Prepaid and Postpaid Utility Analytics
Electricity distribution networks frequently supply prepaid and postpaid consumers through the same infrastructure. However, their billing records represent different types of information, making integrated energy analysis challenging.
This project explores an analytical framework combining postpaid billing information with reconstructed prepaid consumption to support unified demand assessment.
The research considers seasonal demand variation, consumption behavior and comparative energy profiles.
Research objective: Establish a common analytical foundation for examining electricity demand across hybrid prepaid–postpaid distribution systems.
Potential applications: Distribution planning, consumer segmentation and demand-management research.
3. Data-Driven Anomaly Screening for Prepaid Electricity Consumers
Unusual electricity consumption and recharge behavior can arise from legitimate changes in consumer activity, incomplete records, metering irregularities or potential security concerns.
This research investigates statistical and machine-learning techniques for identifying unusual behavioral patterns in prepaid electricity data.
The proposed analytical approach emphasizes interpretable anomaly indicators and the importance of independent verification before assigning any operational significance to flagged records.
Research objective: Explore a systematic approach for detecting consumption and transaction anomalies while minimizing unsupported conclusions about individual consumers.
Potential applications: Data-quality monitoring, risk-based inspection support and future cybersecurity research.
Connecting the Research
These three research directions contribute to a broader goal: developing data-driven and AI-assisted decision-support systems for reliable, efficient and resilient electricity distribution.
Future work will investigate probabilistic demand forecasting, renewable-energy integration and demand flexibility optimization.
Author: Bikash Halder
Research portfolio: IIMEEE — Integrated Intelligence of Mechanical, Electrical and Electronics Engineering