Machine Learning Multivariate Regression Model for Prediction of Heat Gain in Refrigerator Compartment

Autor:	Sunil S Shastri, Prashant Bhat, Bhargav Jain, Vishal S Marathe, Satyanjay Sahoo, Anshu Shukla
Rok vydání:	2019
Předmět:	Multivariate statistics Computer science business.industry Refrigerator car Process (computing) Machine learning computer.software_genre Turnaround time Solar gain Bayesian multivariate linear regression Linear regression New product development Artificial intelligence business computer
Zdroj:	2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA).
DOI:	10.1109/iccubea47591.2019.9128741
Popis:	The study focuses on the process of machine learning multivariate linear regression. The aim of the study is to reduce the computational burden for faster turnaround time for product development cycle. Refrigerator being a common home appliance involves multiple parameters which need to be optimized and involves rigorous simulation strategy. The study presents the simulations strategy that can be promising over experimental test, but with the need this can be improved using machine learning simulations. Study presents results from 3D heat gain, 1D heat gain simulations and results are compared with linear regression simulation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d22605fc297a6a0fd65dc690488f1e88 https://doi.org/10.1109/iccubea47591.2019.9128741 Zobrazit plný text záznamu