Energy Utilization and Prediction using Machine Learning for Improving EMS system: A Study Approach

Abstract

Construction Energy Management System (CEMS) has been an impressive topic these days on account of its importance in lessening energy wastage. In any case, the display of one of CEMS applications which is energy use conjecture has been lifeless due to issues, for instance, low assumption precision. Thusly, this assessment expects to determine the issues by cultivating a judicious model for energy use in cloud-based AI stage. Three frameworks which are Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbor are proposed for the computation of the judicious model. Focusing in on certified application in India, two occupants from a business building are taken as a context oriented examination. The data assembled is examined and pre-arranged before it is used for model getting ready and testing. The introduction of all of the strategies is broke down subject to RMSE, NRMSE, and MAPE estimations. The experimentation shows that every inhabitant's energy use has different apportionment characteristics.