摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-研究人员详细介绍了机器学习的新数据。根据NewsRx Journali Sts在伊利诺伊州厄巴纳的新闻报道，研究表明，“几十年来，建筑节能一直是格力公司天然气减排战略的基石。然而，影响评估表明，节能通常低于目前指导资金决策的工程模型预测。”这项研究的财政支持者包括阿尔弗雷德·P·斯隆基金会、伊林·奥伊斯商务部和经济机会局伊利诺伊州家庭韦瑟里扎蒂援助计划、欧洲研究理事会(ERC)。新闻记者引用了伊利诺伊大学的一句话，“这造成了一个资源分配问题，阻碍了气候变化方面的进展。利用伊利诺伊实施美国大型ST能效计划的数据，我们证明了基于先前实现的结果预测改造影响的数据驱动方法比现状工程模型更准确。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting from Urbana, Illinois, by NewsRx journali sts, research stated, "Building energy efficiency has been a cornerstone of gree nhouse gas mitigation strategies for decades. However, impact evaluations have r evealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions." Financial supporters for this research include Alfred P. Sloan Foundation, Illin ois Department of Commerce and Economic Opportunity's Illinois Home Weatherizati on Assistance Program, European Research Council (ERC). The news correspondents obtained a quote from the research from the University o f Illinois, "This creates a resource allocation problem that impedes progress on climate change. Using data from the Illinois implementation of the U.S.'s large st energy efficiency program, we demonstrate that a data -driven approach to pre dicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models."
NETL
NSTL