Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
