Abstract:
In this paper, I have examined electricity consumption patter and segmentation in
Manikganj which has achieved full electrification but is still struggling with the
issue of arrears. ‘Manikganj Palli Bidyut Samity (MPBS) dataset’ and various
machine learning techniques, such as regression, classification, clustering,
anomaly detection and causality, are applied in order to discover the patterns of
electricity usage, consumer duties and support the efficiency and the awareness.
XGBoost was the best in predicting (≈0.84) the variance of electricity but not the
clusters hung high capacity low usage and low capacity high dues consumer. The
Anomaly detection resurged an anomalous customer usage pattern which can
indicate a suspective fraud or an inefficiency, whereas the causal effect proposed
that the digital meter was associated with lower dues?.. as against analogue meter.
Based on these findings, we have developed a web-based interface that envisages
two views: The administration panel for back end support staff at the district to
evaluate and regulate the data, and the user panel, where the customers can sign
in to check their line capacity, the meter type, the usage report and payment
clearance while a guest get access to the district-wide electricity information for
educational learning and teaching as a learning resource. In sum, the studies and
system exemplify the potential for data-driven intelligence and virtual tools to
shed light on utility management and rural energy use and practices, make visible
and transparent rural energy usage, and raise public awareness of both imaginary
and actual rural energy use.
