The approach to the built environment as a container of human activities has been severely outdated with a new vision that puts the user, his/her well-being and his/her experience, at the centre, shifting from the old “building as a product” to the newer “building as a service” and the newest “building as an experience” concept. Nevertheless, the sick building syndrome, mainly documented in office buildings, is a widespread problem in many public buildings and, among these, in educational facilities. In these categories we can list school and university buildings where the users are not only workers but also students, sharing anyway the same problem: an artificial environment which is not supporting their productivity in the office or learning performance in the classrooms. Carbon dioxide concentration, due to the natural human breathing process, is a diffuse parameter strongly influencing the indoor air quality and, thus, the users’ wellbeing. An acceptance threshold of 1000 ppm in indoor spaces has been derived for this pollutant. Although allowed, the CO₂ concentration in indoor space shouldn’t reach this threshold because in such a condition a 11–23% reduction of the users’ cognitive performance has been measured and, when the level increases reaching 2500 ppm, the reported drop is dramatic (44–94%). Extensive researches set the optimal threshold at 600 ppm, a level defined as fresh air. Then, how is it possible to encompass a natural process as CO₂ production by humans? Basically with the correct ventilation rate, depending on room geometry, number of people and ventilation system (i.e. natural or mechanical or mixed). A modulating mechanical ventilation system could adapt the ventilation rate according to people density and changing indoor conditions nevertheless many existing buildings have outdated systems not providing this option. Sometimes their air handling units (AHUs) have few regulation options to control the parameters and tuning procedures during the building lifecycle are required. CO₂ concentration is convenient to measure indoor air quality because it is easily quantifiable through a sensors network. Thus, it may be adopted as an indicator to assess suitable indoor conditions to human activities and used to trigger manual or automatic procedures to preserve wellbeing thresholds. The article presents a research work depicting the integration of indoor air quality data gathered by internet of things (IoT) sensors to activate the control of the indoor conditions according to the occupancy rate in the educational building eLUX lab, located in the Smart Campus of the University of Brescia. The challenge is to directly regulate the heating, ventilation and air conditioning (HVAC) systems and define opening/closing patterns for windows to improve the indoor air quality and protect the learning performance of users in dynamic use conditions. The embraced methodology suggests the training of an artificial neural network (ANN) with the actual monitored data and to trigger the ventilation rate control through an IoT communication protocol.

一种以人类活动为容器的建筑环境方法已经过时，其新视野使用户，他/她的幸福和他/她的经验处于中心位置，从旧的“建筑产品”转变为”，以更新的“建筑即服务”概念和最新的“建筑即体验”概念。然而，在许多公共建筑中，尤其是在教育设施中，主要在办公楼中记录的病态建筑综合症是一个普遍存在的问题。在这些类别中，我们可以列出学校和大学建筑物，这些建筑物中的用户不仅是工人，而且还是学生，它们仍然面临着相同的问题：一种人造环境，不支持他们在办公室的生产力或教室的学习表现。由于人类自然的呼吸过程，二氧化碳浓度 是一个扩散参数，强烈影响室内空气质量，从而影响用户的健康。对于这种污染物，室内空间的可接受阈值为1000 ppm。尽管允许，但CO₂室内空间的浓度不应达到此阈值，因为在这种情况下，已测得用户的认知能力下降了11–23％，并且当该水平增加到2500 ppm时，报告的下降幅度很大（44–94 ％）。广泛的研究将最佳阈值设置为600 ppm（定义为新鲜空气）。那么，如何将自然过程包含为CO ₂是人类生产的？基本上根据房间的几何形状，人数和通风系统（即自然通风或机械通风或混合通风），选择正确的通风速率。调节式机械通风系统可以根据人的密度和室内条件的变化来调整通风速率，尽管如此，许多现有建筑物的过时系统无法提供此选项。有时，它们的空气处理单元（AHU）具有很少的调节选项来控制参数，并且需要在建筑生命周期内进行调节。一氧化碳₂浓度很容易测量室内空气质量，因为它可以通过传感器网络轻松量化。因此，它可以用作评估适合人类活动的室内条件的指标，并用于触发手动或自动程序以保持幸福感阈值。本文介绍了一项研究工作，该研究工作描述了通过物联网（IoT）传感器收集的室内空气质量数据的集成，以根据位于教育园区eLUX实验室中的教育建筑eLUX实验室的占用率来激活对室内条件的控制。布雷西亚大学。挑战在于直接调节供暖，通风和空调（HVAC）系统，并定义窗户的打开/关闭方式，以改善室内空气质量并保护用户在动态使用条件下的学习表现。包含的方法建议使用实际的监视数据训练人工神经网络（ANN），并通过IoT通信协议触发通风速率控制。