Incremental Learning of Electricity Smart Meter Data

This book explores how electricity consumption in India, still largely tracked through manual meter readings, can be improved using advanced data analytics and cloud-based machine learning. It introduces a proposed system called Cloud4CGMIC (Cloud for Closeness-based Gaussian Mixture Incremental Clustering Algorithm), designed to analyze smart energy meter data more efficiently.

The system uses distributed incremental clustering to detect hidden patterns in electricity usage based on time of day, seasons, and geographic regions. It also helps identify changes in residential energy consumption and supports better forecasting and load management for utility companies.

The book explains how the algorithm works, how incremental learning and knowledge augmentation are achieved, and how it can be implemented using the Microsoft Azure platform. It further demonstrates how to analyze large-scale smart meter data to improve energy planning, reduce inefficiencies, and support carbon emission management.

It is intended for researchers, data and business analysts, and professionals in the energy generation and distribution sector who are interested in smart grid analytics and energy optimization.