Load Curve Analysis and Short-Term Load Forecasting – A Big Data Analytics Approach


Many electrical power distribution companies face challenges in forecasting the amount of electricity needed to meet consumer demand. The emergence of smart grid technologies has led to a paradigm shift in the electrical system. Companies are installing smart plugs in households, acting as proxies between wall outlets and connected devices. Smart meters record energy consumption and communicate with suppliers for monitoring and billing. Users include households, office buildings, and restaurants. Our analysis is based on data from smart plugs in private households, collected every minute for 30 days.

In this paper, we present a big data analytics approach to aid customers and power distribution companies. We build a short-term load forecasting model that generates real-time alerts when consumption reaches a threshold. This enables real-time analysis of consumer usage, ensuring safe and economical operation of power systems. Additionally, it lays the groundwork for electricity dispatching schedules and transactions.

Published in: Proceedings of the Sixth International Conference on Business Analytics and Intelligence, December 2018, IISc, India.


Dinesh Ghanta

Pavanraj Talawar

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