首页|Study Results from Ningbo University Provide New Insights into Machine Learning (A Sensitivity Analysis Method Combining Dempster-shafer Theory And Machine Lear ning For Energy-saving Evaluation of Building Occupant Behavior)
Study Results from Ningbo University Provide New Insights into Machine Learning (A Sensitivity Analysis Method Combining Dempster-shafer Theory And Machine Lear ning For Energy-saving Evaluation of Building Occupant Behavior)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from Ningbo, People’s Republic of Chin a, by NewsRx journalists, research stated, “ABSTRACT: For a very long time, the research of the sensitivity analysis of occupant behavior to energy assessment h as been in the spotlight.” Our news correspondents obtained a quote from the research from Ningbo Universit y: “The key element of the research is determining the exact probability of occu pant behavior uncertainty. However, due to the specificity of occupant behavior, data on occupant behavior from different independent sources of information can differ significantly. This paper explores the use of Dempster-Shafer theory to the sensitivity analysis of energy evaluation of occupant behavior in buildings. The Dempster-Shafer theory is an imprecise probability theory that allows the s ystem to create assumed confidence intervals based on interval values probabilit y combined with knowledge of uncertainty factors from many different sources of information. The findings show that the data processing approach based on Dempst er-Shafer theory provides effective and reliable information for evaluating ener gy related to human behavior in buildings. To begin with, the sensitivity analys is process might be accelerated by applying machine learning to process the data .”
Ningbo UniversityNingboPeople’s Repu blic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningMathemati cal Theories
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