摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一份新报告的主题。根据NewsRx记者来自沙特阿拉伯N Ajran的新闻报道，研究表明，"多年来，建筑电器已经成为改善室内人工智能质量和居住者生活方式的主要能源消费者。"这项研究的资助者包括纳日兰大学的科学研究主任，在杰出的研究资助计划下资助这项工作。新闻记者从纳日兰大学的研究中获得了一句话："由于控制操作活动不当，建筑部门的一次能源使用，特别是照明、供暖、通风和空调(HVAC)设备，预计在未来几年将翻一番。最近，一些研究人员提供了一种自动解决方案，在空间被占用时打开HVAC和照明，在空间空置时关闭。以前的研究表明，缺乏可公开访问的环境感知数据集，并建议开发检测建筑物占用情况的整体模型。此外，他们的解决方案的可靠性往往会随着建筑物占用率的增加而降低。因此，本文提出了一种基于机器学习的智能建筑入住率检测算法,该算法除了考虑已有的研究中使用的暖通空调参数外,还考虑了照明参数,并采用参数分类器保证预测参数与入住率预测模型之间的强相关性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from N ajran, Saudi Arabia, by NewsRx correspondents, research stated, “Over the years, building appliances have become the major energy consumers to improve indoor ai r quality and occupants’ lifestyles.” Funders for this research include Deanship of Scientific Research At Najran Univ ersity For Funding This Work Under The Distinguished Research Funding Program.The news journalists obtained a quote from the research from Najran University: “The primary energy usage in building sectors, particularly lighting, Heating, V entilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, seve ral researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previou s studies indicate a lack of publicly accessible datasets for environmental sens ing and suggest developing holistic models that detect buildings’ occupancy. Add itionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based fr amework for smart building occupancy detection that considered the lighting para meter in addition to the HVAC parameter used in the existing studies. We employe d a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model.”
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