摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx记者在英国利物浦的新闻报道，研究表明：“有许多人工智能(AI)解决方案可用于实现热舒适。他们要么使用有限的数据集进行培训，要么使用有限的现场研究进行个性化培训。”新闻记者从利物浦约翰·莫尔斯大学获得了这项研究的一句话，本文以ASHRAE Multiple DA Tabases作为浅层监督学习数据集,设计了一种适用于住宅节点的基于(ANN)的人工神经网络控制器,其学习精度可提高到96.1%。结果表明,该模型能适应不同环境,能较好地反映人体的适应性舒适度,能代表ASHRAE Standa RD 55标准数据的98.90%,比以往的研究提高6.06%。更广泛的舒适区认知可以通过降低温度设定点来实现节能，同时保持舒适性。这项研究还证明，占领者的存在、日程安排和活动可以进一步节省能源。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Artificial Intell igence are discussed in a new report. According to news reporting from Liverpool , United Kingdom, by NewsRx journalists, research stated, “Numerous Artificial I ntelligence (AI) solutions are available for achieving thermal comfort. They wer e either trained with limited datasets or using personalized training with limit ed field studies.” The news correspondents obtained a quote from the research from Liverpool John M oores University, “This work assessed the model that used the ASHRAE multiple da tabases as the shallow supervised learning dataset for an Artificial Neural Netw ork (ANN) based controller suitable for the residential dwellings’ node. The lea rning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the pr ior 1970s, the new, modular, refurbished, and the use of new materials to improv e indoor thermal performance. The result shows that the model was able to perfor m in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standa rd 55 data, 6.06% improvement from the previous research. As a res ult, the broader comfort zone acknowledgement can lead to energy saving whilst m aintaining comfort by the possibility of lowering the temperature set point. Thi s study also proves that further energy savings can be acquired from the occupan ts’ presence, scheduling, and activities.”
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