Indoor air quality and thermal comfort optimization in classrooms developing an automatic system for windows opening and closing

Highlights

• Environmental parameters triggering users’ actions on windows were assessed.

• An automatic system for windows opening was developed to achieve IEQ in a classroom.

• The system was driven by an adjusted version of Humphreys’ adaptive algorithm.

• The algorithm was adapted including CO₂ concentration and reducing the dead band.

• The system guarantees low CO₂ levels, thermal comfort and users satisfaction.

Abstract

Thermal comfort and indoor air quality in school classrooms are essential requirements to promote students’ productivity and reduce health symptoms. This paper presents the development of an automatic system for window openings, based on thermal comfort and indoor air quality correlations. The research was carried out in two adjacent classrooms. The initial phase aimed at assessing environmental conditions in classrooms, testing objective and subjective comfort models and establishing trigger parameters for window opening events; the second phase regarded the implementation of an adaptive control algorithm in an automatic system piloting windows with the aim of maintaining a satisfactory environment both in terms of IAQ and thermal comfort. The main results show that: (1) the IAQ is a relevant issue in school classrooms, because students usually suffer high CO₂ levels; (2) the stronger driving force for undertaking adaptive actions is thermal comfort, while the need to improve the air quality is a secondary constraint; (3) the mechanized system ensures a good quality in terms of IAQ, thermal comfort and users’ satisfaction.

Introduction

The main target to be achieved in school classrooms is preserving students’ attention, efficiency and health while attending the lessons. From an environmental perspective it can be translated into reaching and maintaining a satisfactory perception in terms of air temperature and indoor air quality. Indeed, students spend almost all day in indoor environments and mainly inside classrooms with high occupant density [1], [2], [3]. Several studies show that inadequate ventilation rates can lead to a decrease in users’ performances and growing absenteeism [4], [5] and that adaptive actions, to restore comfortable conditions, usually occur when the situation is already critical, especially in relation to CO₂ concentration [6], [7].

During the recent years different adaptive control algorithms (named ACA in the paper as in [8]) have been developed with the aim of predicting human behaviours, usually in relation to a specific action (e.g. windows or blind use, light-switching) [9], [10], [11], [12]. The “adaptive” term means that these algorithms are based on the adaptive comfort theory, stating that: “if a change occurs such as to produce discomfort, people react in ways which tend to restore their comfort” [13]. This principle focuses on the human-building interaction, since it asserts that if people perceive the ambient as uncomfortable, they will try to improve their thermal comfort, operating both on building controls (e.g. using doors, windows, blinds and fans) and on their personal condition (e.g. changing their clothes or taking hot or cold drinks). Many authors demonstrated that the adaptive model is the most proper and realistic approach for naturally ventilated buildings. Humphreys and Nicol, assessing the validity of the Fanger steady-state comfort model [14], [15], concluded that a ISO 7730 [16] compliant approach is more likely to bring to erroneous evaluations of thermal discomfort because the model poorly reflects the human thermal adaptation [17], [18]. Similar results were obtained by de Dear and Brager [19] and confirmed by many other researches [20], [21].

This active-user comfort approach is based on the relationship between the indoor temperature and the comfort one. Humphreys and Nicol assessed that the comfort temperature is a function of the outdoor average temperature and proposed two equations to derive it in free-running buildings [22]. The proposed equations were tested, tuned and validated in different climatic areas [8] and were also adopted by various Standards (ASHRAE, ATL CEN, CIBSE).

The majority of ACAs proposed in the last decades concerns the window use, a pattern widely studied because it is one of the most recurring users’ adaptive action [23]. The ACAs were usually developed for residential [24], [25], [26] and commercial buildings [9], [10], [27], [28], while the academic environment was rarely analysed [6].

Among these ACAs, the Humphreys adaptive algorithm [11] (the logical structure is reported in Table 1 part a) was selected for the present research since it is based on the widely-accepted adaptive comfort theory and its use in real applications requires the recording of few environmental variables.

The possible adjustments refer to the scholastic building use. In fact, the algorithm was tailored on the specifics of office buildings and it is only driven by temperature inputs, so it doesn’t consider metabolic and clothing variables as well as CO₂ concentration. In school classrooms, like in offices, the met and clo parameters are rather constant [29]. In fact, the activity level is the sedentary one (met: 1.1 according to ISO 7730 Table B.1) and the clothing level among the students is very similar along each season [30]. On the contrary, the CO₂ concentration is one of the major issues in rooms with high occupational density [31].

Indoor air quality in school classrooms is a global issue [32]: various studies developed in many countries [33], [34], including Italy [35], [36], remark poor indoor ventilation rates and high CO₂ levels [37], [38], [39], [40] and even European projects are focused on this topic [41], [42]. However, the evidence of the problem is not followed by satisfactory solutions [43], especially for natural ventilated buildings that can’t benefit from mechanical ventilation to enhance the IAQ.

According to the contextual lacks, the main goal of the present work was the development of an automatic window-operating system driven by an adaptive control algorithm that, combining both temperature and CO₂ concentration inputs, could guarantee a healthy and thermally comfortable environment in naturally ventilated schools. Moreover, the present study is included in a wider research carried out in the same school along several years. The experimental data collected from previous monitoring campaigns provided solid working bases and were used as lines guide for the survey development.