Open AccessUrja Analysis using Machine Learning and Big Data
Author(s) -
Sankalp Gupta,
Purnansh Billore,
Neha Pagar,
Vijaylaxmi Bittal
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1705
Subject(s) - smart grid , electricity , computer science , big data , demand side , consumption (sociology) , grid , power grid , dynamic pricing , environmental economics , electricity demand , power (physics) , electricity generation , business , data mining , engineering , electrical engineering , economics , marketing , social science , physics , geometry , mathematics , quantum mechanics , sociology
The electricity grid usually overloads or is under loads in different locations at different times which essentially leads to wastage of power and poor power distribution(in rural and semi rural areas). There is a need for a system which can smartly predict energy consumption and reduce the load on grids based on the consumer's usage pattern. Also there is a need for specific recommendations for particular consumers to help them reduce their bills. Keywords—Big data Analysis, Machine Learning, Smart grid, Energy forecasting, Demand side management, Dynamic Time-of-Use electricity pricing.