Energy Prediction of Appliances using Supervised ML Algorithms

Robbia Gulnar Robbia

doi:10.32350/icr.0201.05

Robbia Gulnar Robbia Department of Computer Science, Government College of Faisalabad (GCUF), Punjab, Pakistan

DOI: https://doi.org/10.32350/icr.0201.05

Keywords: Supervised, machine learning, Root mean square error (RMSE), energy consumption, prediction energy utility

Abstract

Abstract Views: 71

The amount of energy consumed by domestic appliances is an important area of research. Hence, the main goal of this study is to produce very precise forecasts about energy consumption by home appliances using the least amount of processing power. The algorithms used in this study for predicting energy usage/consumption included regression, K-nearest neighbor, decision trees, and random forest. These algorithms were applied on the appliances’ energy prediction dataset made available for public use at the UCI Machine Learning Repository. To compare the data sets and choose the optimal machine learning (ML) algorithm for them, root mean square error (RMSE) was computed.

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