Electricity consumption has been extensively studied in the computer architecture field since many years. While the acquisition of energy as a measure in machine learning is emerging, most of the experimentation is still primarily focused on obtaining hoisted levels of accuracy without any computational restrictions. We believe that one of the grounds for this shortage of interest is due to their absence of ease with access to evaluate energy consumption. The main objective of this study is come to evaluate useful regulations to the machine learning community that permits them the fundamental realization to use and build energy estimation methods for machine learning algorithms. Use of different ensemble models like Linear Regression, Support Vector Regression, and XG boost regression to predict the electricity and to obtain accurate results. However, we also present the up-to-date software tools that grant electricity estimation principles, together with two use cases that strengthen the inquiry of energy exhaustion in machine learning. At the end, we predict the future energy which is so helpful to the grid to make accurate energy for the grid by updating with smart meters where everybody can know the people, who are using more energy in what appliances, so it is immensely helpful in which time we need more energy and less energy. Keywords: Electricity, Machine Learning, Linear Regression, Support Vector Regression, XG Boost regression.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL