# Windows, fans, and solar shadings during summer and heatwave: Occupant behavior and potential for improvement in heat-mitigation practices

Kevin Campagna, Anaïs Machard, Aurélie Foucquier, Dorothée Charlier, Monika Woloszyn

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Kevin Campagna, Anaïs Machard, Aurélie Foucquier, Dorothée Charlier, Monika Woloszyn. Windows, fans, and solar shadings during summer and heatwave: Occupant behavior and potential for improvement in heat-mitigation practices. Building and Environment, 2026, 290, pp.114168. 10.1016/j.buildenv.2025.114168. cea-05447564

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# Windows, fans, and solar shadings during summer and heatwave: Occupant behavior and potential for improvement in heat-mitigation practices

Kevin Campagna<sup>a,b,c,∗</sup> <sup> ID </sup>, Anaïs Machard<sup>b</sup>, Aurélie Fouquier<sup>c, ID</sup>, Dorothée Charlier<sup>d</sup>, Monika Woloszyn<sup>a, ID</sup>

## ARTICLE INFO

### Keywords:

- Free-running building

- Passive cooling strategies

- Adaptive behavior

- Residential building

- Field study

- Monitoring campaign

- Awareness campaign

## ABSTRACT

Occupant behavior during summer and heatwaves has a strong impact on overheating in free-running residential buildings. Yet, the use of windows, solar shadings, and fans remains poorly studied. This study addresses this gap through the largest monitoring campaign of its kind in France on this topic, covering 76 dwellings across three regions in summer 2023. Window states, solar shading positions, fan use, and indoor and outdoor conditions were systematically monitored for four months. Behaviors were analyzed individually and in combination to better understand real behavior in this context and assess the adoption of heat-mitigation practices and their potential for improvement.

Results reveal substantial diversity across dwellings and through summer periods. General tendencies are consistent with previous datasets from other countries and contexts, with two main differences. Fan use is over four times lower at similar indoor and outdoor conditions, likely reflecting cultural factors. Unlike air-conditioned buildings, no cutoff temperature is observed for night-time window opening, reflecting a distinct night cooling strategy. During heatwaves, adaptive behaviors are adopted, yet the potential for improvement averages 40 % and ranges from 15 % to 75 % across dwellings. The largest potential of improvement lies in closing windows during the day when outdoor temperatures exceed indoor ones, whereas heat-mitigation practices are more consistently adopted at night.

These findings provide a foundation for developing needed, more realistic occupant behavior models for French residential context during summer and heatwave. These results provide guidance for future awareness campaigns promoting passive heat-mitigation practices of occupants, emphasizing their importance.

## 1. Introduction

Due to climate change, the frequency and intensity of heatwaves is expected to increase in the coming years [2]. This raises significant challenges related to overheating in building environments and summer thermal discomfort. Particularly in countries like France, where 75 % of dwellings are not equipped with air conditioning [1], making them particularly vulnerable to summer heatwaves. Letting, most building occupants rely on passive cooling strategies such as windows opening for nocturnal ventilation, use of external solar shadings, and fans to maintain comfortable indoor environments. In free running buildings, occupant behavior with window, solar shading, and fan use plays a key

role in either mitigating or exacerbating overheating and discomfort [24]. Studies have shown that accurately representing natural ventilation is crucial for obtaining reliable overheating assessments when using building performance simulations [37]. Inadequate consideration of occupant behavior may result in the design and renovation of buildings that are thermally uncomfortable for occupants during heatwaves.

However, real occupant behavior during summer and heatwaves remains unknown and poorly understood. Numerous studies have examined how occupants interact with windows [8,12,30,38], solar shadings [10,39,42] and fans [15,19,33]. Most studies monitor dwellings or offices using sensors to capture occupant behavior [12,30], while others rely on self-reported surveys in combination of indoor/outdoor

<footnote><sup>a</sup> Université Savoie Mont-Blanc, CNRS, LOCIE, 73000 Chambéry, France</footnote>

<footnote><sup>b</sup> Centre Scientifique et Technique du Bâtiment (CSTB), 24 Rue Joseph Fourier, 38400 Saint-Martin-d'Hères, France</footnote>

<footnote><sup>c</sup> Univ. Grenoble Alpes, CEA, Liten, INES, 73375 Le Bourget-du-Lac, France</footnote>

<footnote><sup>d</sup> Université Savoie Mont-Blanc, IREGE, 4 chemin de Bellevue, 74940 Annecy-le-Vieux, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: kevin.campagna@cstb.fr (K. Campagna).</footnote>
monitoring [15,19]. These studies typically analyze behavior in relation to indoor or outdoor temperature, and sometimes differentiate between dwelling [35], room types [18], or times of day [19], but rarely investigate all these aspects simultaneously. It is well established that occupant behavior is influenced by indoor and or outdoor conditions, with window operation often showing a cut-off temperature around 20–25 °C outdoors [11,19]; however, considerable variability in observed behaviors remains across studies. This variability may arise from differences in context (e.g., cultural, climatic or from buildings) or in monitoring or analysis methodologies (e.g., duration, period, number of dwellings)—and is more likely due to a combination of both [13]. Despite these studies, three major gaps remain. First, very few studies investigate occupant behavior during summer and heatwaves, and those that do often focus on air-conditioned buildings or offices rather than free-running residences [9,15,31]. Second, most research focuses on individual behavior without considering their combination [5], which can critically affect building overheating in summer. Third, there is a lack of data for French free-running residential buildings during summer: only two experimental studies have been identified [14,34], but their datasets are unavailable and cover very few dwellings, limiting generalization. A similar lack of this type of data exists internationally [6].

Knowledge of occupant behavior during summer and heatwaves in the French context can improve the accuracy and realism of behavior model in building performance simulations. It also helps identify the potential for improvement in heat mitigation practices to help maximize the free-running potential of buildings. Finally, these insights can guide targeted awareness campaigns promoting heat-mitigation practices by identifying commonly adopted behaviors and those that need greater promotion. Thus, the study focuses on the following research questions:

* **RQ1:** How do French occupants interact with windows, solar shadings, and fans during summer and heatwaves?

* **RQ2:** How does French occupant behavior differ from those in other countries and contexts?

* **RQ3:** What is the potential for improvement in occupant behaviors related to heat-mitigation practices?

To address these research questions, this study relies on a robust and comprehensive dataset—the largest of its kind in France—which represents a major strength of this work. It includes the monitoring of 76 dwellings across three distinct regions in France. From June to September 2023, detailed observations were systematically recorded, capturing window states, solar shading states, fan use, as well as indoor and outdoor environmental conditions. This dataset forms the backbone of our analysis, enabling an exploration of occupant behavior during summer and heatwaves. We examine the use of windows, solar shading and fans, compare these behaviors with other datasets, and assess the potential for improvement in heat-mitigation practices by categorizing behaviors and analyzing their combinations.

# 2. Data collection

## 2.1. Instrumentation details

### 2.1.1. Project framework and occupants' recruitment

The dataset analyzed in this study was collected within the framework of the RENOPTIM project, conducted by the Centre Scientifique et Technique du Bâtiment (CSTB) in collaboration with the Union Sociale pour l'Habitat (USH). This project aims to reduce reliance on active cooling systems in residential buildings across France.

A total of 76 apartments were recruited and monitored from the 1st of June 2023 and the 30th of September 2023, in order to analyze the thermal conditions in indoor environments during summer and heat-wave periods. Instrumented dwellings were non-air-conditioned collective housing in urban or semi-urban areas, representing diverse

building types and insulation levels and orientations. Occupants were required to be present for most of the summer monitoring period; participation was voluntary, with financial compensation provided.

### 2.1.2. Sensors installed

The principal rooms were equipped with temperature and humidity sensors, and most of the windows and doors were fitted with magnetic contact sensors to detect their state: 'open' or 'close', with a total of 298 rooms instrumented. A luminosity sensor was placed at the center of the window, facing the exterior to estimate the position of external solar shading during the day. To conduct a longitudinal survey—particularly of thermal comfort, sensation, and satisfaction—each dwelling was equipped with a 'thermal comfort box', typically installed in the living room, which one occupant was instructed to respond to three times per day. This device collected temperature and humidity data within the comfort box. Smart plugs were used to monitor the power and usage periods of portable fans, and wattmeters were used to measure the global electricity consumption of the dwellings. In addition, building thermal characteristics were collected through a French Energy Performance Certificate (EPC), and a survey composed of close-ended questions about occupant-related factors (habits and sociological profiles) was conducted.

## 2.2. Overview of the dataset

### 2.2.1. Buildings and occupants' characteristics

As highlighted by Vellei et al., [40], many studies do not adequately describe occupant socio-economic or building characteristics. The database and this section address these gaps by providing additional context. The 76 monitored dwellings are distributed across three regions of France, corresponding to distinct climate zones: 40 dwellings (53 %) in the Paris area (IDF, temperate oceanic, Cfb, ASHRAE 4A), 22 dwellings (29 %) in the Southeast (SE, Mediterranean, Csa, ASHRAE 3A), and 14 dwellings (18 %) in the Southwest area (SW, temperate oceanic, Cfb, ASHRAE 3A).

The dataset includes 45 private-sector dwellings (59 %) and 31 social housing units (41 %), with a higher concentration of social housing in the SE region (17 over 22 dwellings). Most dwellings (80 %) are equipped with a pedestal or ceiling fan, while five dwellings have a portable air conditioner that is used only occasionally. Fifteen dwellings do not have solar protection. Dwelling sizes range from one-bedroom apartments in Paris to over 140 m² (Fig. 1), and most dwellings are located on intermediate floors (Fig. 2). Number of occupants per dwelling varies from single residents (38 %), 2–3 occupants (38 %), to four or more (24 %). Nighttime noise levels are reported as quiet in 57 % of dwellings (mostly in the SE region), moderately noisy in 33 %, and very noisy in 10 %, with none in the Southwest.

Building characteristics are summarized using the French EPC Energy Label (Fig. 3) and described in [22]. The Paris metropolitan area

<figure><img src="image_2.png" /><figcaption>Fig. 1. Habitable surface area across the regions.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Distribution of housing levels across the regions.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Energy label distribution across the three regions. Labels A–G correspond to annual energy consumption categories, with A representing the most energy-efficient buildings and G the least efficient.</figcaption></figure>

(IDF) includes a wide range of building types, from well- to poorly insulated, while SW and SE are labeled E or higher. It should be noted that SW buildings are generally better insulated than SE buildings, as the energy label is based on winter climate: warmer conditions in SE result in higher energy consumption (and thus a better label) for the same insulation level.

### 2.2.2. Weather description

Fig. 4 shows the average hourly regional temperatures from early June to late September 2023, as recorded by roof-mounted weather stations installed near most building residences. Apartments with no recording were linked to the nearest available meteorological station. The region with the highest observed temperatures was the Southeast, where maximum temperatures exceeded 40 °C. The Southwest had the most humid heatwave conditions, with humidity levels reaching up to 80 %, compared to 70 % in the Paris metropolitan area and 60 % in the Southeast. Global solar radiation was much higher in the Southwest and Southeast compared to the Paris metropolitan area. Several heatwave periods, approximately two per region, occurred during the summer of 2023. For the purpose of this study, when speaking about a heatwave period we chose to focus on a single 7-day period per region, the longest and hottest period identified using the heatwave identification method from Ouzeau et al., [29]. The classical summer period refers to the entire summer dataset excluding these 7 heatwave days. The urban heat island (UHI) effect was analyzed for each weather station by comparing the temperatures at these stations with those recorded by MétéoFrance stations located outside urban areas. During heatwaves, in the Southwest an average urban heat island effect of approximately +1.5 °C was observed at night, in IDF region, the more urbanized area of the study, temperatures rose in average by up to +4 °C both day and night, while in the Southeast, the UHI effect was +2 °C during both day and night.

## 3. Data analysis methods

Fig. 5 summarizes the different research steps of this study.

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Hourly local outdoor temperatures averaged across weather stations within each region. Identified heatwave periods are highlighted in red. Top: SW for South-West of France, Middle: IDF for Paris metropolitan area (north of France), Bottom: SE for South-East region.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 5. General methodology, authors’ elaboration.</figcaption></figure>

## 3.1. Data processing

### 3.1.1. Data curation

Data quality was verified in two phases: during and after instrumentation. During monitoring, a two-person team performed daily checks, while automated scripts flagged anomalies such as missing or constant sensor values. Occupants or technicians intervened as needed to reset sensors or replace faulty equipment, ensuring high data quality and coverage. After instrumentation, inconsistent values were removed and gaps shorter than the defined threshold were interpolated using a Python-based algorithm. Table 1 details the variable-specific thresholds and interpolation criteria. Data at a given time step were considered inconsistent and excluded if they fell outside the range [Threshold min; Threshold max]. Periods with constant values exceeding the maximum allowed duration (Time constant max) were also removed. Missing data

within a maximum gap (Interpolation time max) were interpolated according to the specified method (Type of interpolation).

### 3.1.2. Occupancy

Daily occupancy was estimated based on the presence of at least one occupant during the day. A day was defined as occupied and kept in the analysis if it meets one of the two following conditions: a change in window or door state (open → closed or closed → open) in the dwelling, or a response to the ‘thermal comfort box’ present in the dwelling. This approach effectively excludes periods of prolonged unoccupancy, such as holidays or long absences, from the analysis. However, it does not allow for the determination of occupancy at an hourly scale. Consequently, periods when occupants were temporarily absent (e.g., at work) remain included. Although this represents a limitation of our experimental campaign (Section 5.1), it can also be seen as an opportunity to

<table><caption>**Table 1**
Thresholds and interpolation hypothesis per variable to ensure data quality.</caption><thead><tr><th>Variable</th><th>Threshold min</th><th>Threshold max</th><th>Type of interpolation</th><th>Interpolation time max [hour]</th><th>Time constant max [hour]</th></tr></thead><tbody><tr><td>Indoor Temperature</td><td>0 °C</td><td>50 °C</td><td>Time</td><td>1</td><td>12</td></tr><tr><td>Outdoor temperature</td><td>0 °C</td><td>50 °C</td><td>Time</td><td>1</td><td>12</td></tr><tr><td>Contact sensors (window)</td><td>0</td><td>1</td><td>Last value</td><td>1</td><td>None</td></tr><tr><td>Connected plug (Fans)</td><td>None</td><td>None</td><td>Time</td><td>1</td><td>336</td></tr><tr><td>Luminosity (Solar shading)</td><td>0</td><td>100,000</td><td>Time</td><td>1</td><td>None</td></tr></tbody></table>
analyze daily window and solar shading patterns, since their positions during unoccupied periods may subsequently influence the indoor environment when occupants are present.

### 3.1.3. Determination of fan state

Fan use was monitored via smart plugs measuring electricity consumption. Occupants were asked not to unplug fans to ensure continuous monitoring, though compliance could not be guaranteed. Fans and mobile air-conditioning units were differentiated by their energy profiles. Consumption peaks caused by irregular transmission intervals were smoothed by aggregating power data to an hourly resolution. Fans were considered switched on when hourly average consumption fell between 10 W (small/low-speed fans) and 120 W (large fans).

### 3.1.4. Determination of solar shading state

Luminosity sensors were installed on the middle height of the windows, towards the exterior, to determine the solar shading position, limiting analysis to daylight hours (7:00 a.m.–10:00 p.m.). Luminosity values were converted into open/closed states using a conservative threshold of 100 lux, ensuring that values below this threshold indicate that the solar shading is indeed closed. Above this threshold, the solar shading may be fully or partially open.

### 3.1.5. Spatial aggregation of window and solar shading states

When two windows or solar shadings were present and monitored in the same room, the room was considered as having the window (or solar shading) open if at least one was open and closed only if both were closed. No aggregation at the dwelling level was performed; each room was considered independently and separately. The limitations of this approach are discussed in Section 5.3.

## 3.2. Analysis

### 3.2.1. Indicators to understand occupant behaviors

To analyze occupant behavior in the dataset (RQ1), key indicators from previous studies [7,25,40] were used. The selected metrics include:

* **Windows:** Daily opening duration [hour/day] and proportion of open windows [-];

* **Solar shading:** Daily closing duration [hour/day] and proportion of solar shading closed [-];

* **Fans:** Daily usage duration [hour/day] and proportion of fans switched on [-].

The daily opening/closing/usage duration is determined for each day per room and aggregated in a boxplot per dwelling. The proportion of each state is analyzed as a function of indoor and outdoor temperatures, time of day, and across a one-week heatwave period compared with the remainder of the dataset, separately for each region.

### 3.2.2. Comparison with others dataset

To address RQ2, occupant behaviors observed during the campaign were compared with models from other contexts (e.g., countries, building types, climates) to highlight French-specific patterns, reinforce general behavioral trends, and provide a broader understanding of summer occupant behavior.

Seventeen models from the literature were selected for comparison—eight for window operation, three for solar shading, and six for fan usage (Table 2). Selection criteria included consideration of outdoor temperature, coverage of summer periods, and either wide recognition in the literature [12] or contextual similarity to the present dataset (e.g., [15,19]). Following the methodology of [19] and [18], occupant behavior was modeled using logistic regression with a quadratic term for outdoor temperature, capturing the cut-off behavior observed in Section 4.2 (Eq. (1)–2). Models were implemented using the Logit function from the statsmodels library in Python 3.11.5.

$$ \operatorname{logit}(P) = a + b_1 \cdot T_{\text{out}} + b_2 \cdot T_{\text{out}}^2 \quad (1) $$

$$ P = \frac{1}{1 + e^{-\operatorname{logit}(P)}} \quad (2) $$

It should be noted that the model proposed in [11] includes higher-order polynomial terms, extending up to the fourth degree (e.g.,

<table><caption>**Table 2**

Literature models names and characteristics for comparison.</caption><thead><tr><th>Behavior</th><th>Reference</th><th>Type of building</th><th>Size sample</th><th>Country</th><th>Particularity</th><th>Type of model</th><th>Model name</th><th>a</th><th>b1</th><th>b2</th></tr></thead><tbody><tr><td rowspan="10">Window</td><td>[12]</td><td>Office</td><td>40</td><td>Switzerland</td><td>None</td><td>Uni</td><td>Haldi 2008, Office, Swi</td><td>-1.12</td><td>0.049</td><td>0</td></tr><tr><td>[11]</td><td>Office</td><td>14</td><td>Switzerland</td><td>None</td><td>Poly 4th degree</td><td>Haldi 2009, Office, Swi, Poly</td><td>-2.275</td><td>0.0545</td><td>-0.000070</td></tr><tr><td rowspan="3">[19]</td><td rowspan="3">Residential</td><td rowspan="3">42</td><td rowspan="3">Australia</td><td>Day time (6AM–6PM)</td><td>Poly</td><td>Kim 2017, Res, Aus, Day</td><td>-13.34</td><td>1.119</td><td>-0.022</td></tr><tr><td>Night-time (6PM–6AM)</td><td>Poly</td><td>Kim 2017, Res, Aus, Night</td><td>-22.72</td><td>1.998</td><td>-0.042</td></tr><tr><td>All day</td><td>Poly</td><td>Kim 2017, Res, Aus, All day</td><td>-12.07</td><td>0.995</td><td>-0.0193</td></tr><tr><td>[15]</td><td>Residential</td><td>120</td><td>Japan</td><td>All day</td><td>Uni - adjusted</td><td>Imagawa 2020, Res, Jap, All day</td><td>-6.3</td><td>0.330</td><td>0</td></tr><tr><td rowspan="2">[18]</td><td rowspan="2">Residential</td><td rowspan="2">41</td><td rowspan="2">Australia</td><td>Living room</td><td>Poly</td><td>Jeong 2022, Res, Aus, Living room</td><td>-8.74</td><td>0.67</td><td>-0.012</td></tr><tr><td>Bedroom</td><td>Poly</td><td>Jeong 2022, Res, Aus, Bedroom</td><td>-4.75</td><td>0.36</td><td>-0.007</td></tr><tr><td rowspan="3">Solar shading</td><td>[26]</td><td>Office</td><td>-</td><td>UK</td><td>None</td><td>Uni</td><td>[26], Office, UK</td><td>1.08</td><td>-0.022</td><td>0</td></tr><tr><td>[12]</td><td>Office</td><td>25</td><td>Europe</td><td>None</td><td>Uni</td><td>[26], Office, EU</td><td>0.3</td><td>-0.005</td><td>0</td></tr><tr><td>[12]</td><td>Office</td><td>31</td><td>Switzerland</td><td>None</td><td>Uni</td><td>Haldi 2008, Office, Swiss</td><td>-3.54</td><td>0.139</td><td>0</td></tr><tr><td rowspan="5">Fan</td><td>[26]</td><td>Office</td><td>11</td><td>Europe</td><td>None</td><td>Uni</td><td>[26], Office, EU</td><td>-3.80</td><td>0.110</td><td>0</td></tr><tr><td>[12]</td><td>Office</td><td>37</td><td>Switzerland</td><td>None</td><td>Uni</td><td>Haldi 2008, Office, Swi</td><td>-8.18</td><td>0.311</td><td>0</td></tr><tr><td>[19]</td><td>Residential</td><td>42</td><td>Australia</td><td>Day time (6AM–6PM)</td><td>Uni</td><td>Kim 2017, Res, Aus, Day</td><td>-4.68</td><td>0.11</td><td>0</td></tr><tr><td></td><td></td><td></td><td></td><td>Night-time (6PM–6AM)</td><td>Uni</td><td>Kim 2017, Res, Aus, Night</td><td>-5.40</td><td>0.15</td><td>0</td></tr><tr><td></td><td></td><td></td><td></td><td>All day</td><td>Uni</td><td>Kim 2017, Res, Aus, All day</td><td>-4.79</td><td>0.11</td><td>0</td></tr><tr><td></td><td>[15]</td><td>Residential</td><td>120</td><td>Japan</td><td>All day</td><td>Uni - adjusted</td><td>Imagawa 2020, Res, Japan</td><td>-7.3</td><td>0.322</td><td>0</td></tr></tbody></table>
$$b_1 = 0.000386 \text{ et } b_1 = 0.0000112).$$

### 3.2.3. Assessing the potential for improvement in occupant heat-mitigation practices

To address research questions RQ1 and RQ3, behaviors with window (Section 3.2.3.1) and with solar shading (Section 3.2.3.2.) are categorized, and the potential for improvement in occupant heat mitigation practices is assessed both individually and in combination (windows + solar shading) (Section 3.2.3.3) during the heatwave period. In addition, the combination of fan use, and windows is examined by analyzing window categories when fans are switched on.

#### 3.2.3.1. Windows categories.
Window behaviors are categorized based on the indoor-outdoor temperature difference and the window state, resulting in five categories (Table 3). For example, the category “protected” corresponds to windows being kept closed when the outdoor temperature is higher than the indoor temperature. Categories which are considered as heat-mitigation practices are defined as thermally optimal window behaviors: preventing warm outdoor air from entering when it is hotter outside and allowing cooler outdoor air in when it is hotter indoor. The potential for improvement in window-related heat-mitigation practices (Pot<sub>window</sub>) is therefore defined as (eq (3)):

$$Pot_{window} = 1 - \sum_{i \in \text{Heat mitigation practices (window)}} p_i \quad (3)$$

where p<sub>i</sub> represents the proportion of heat mitigation practices identified in Table 3. A conservative threshold of 26 °C was selected, as temperatures below this value are not considered to cause overheating or discomfort. This corresponds to the lower discomfort threshold defined in the French regulation (RE2020).

#### 3.2.3.2. Solar shadings categories.
To simplify the combined analysis of shading and window behaviors (Section 3.2.3.3), and given that the instrumentation records shading states only between 7 a.m. and 10 pm. (see Section 3.1.4), shading behaviors are analyzed during periods when T<sub>in</sub> ≤ T<sub>out</sub>. This corresponds to situations in which windows are categorized as heating or protected, which occur almost exclusively during the daytime during heatwave period. This period is hereafter referred to as day.

A window is considered exposed to potential solar gains when its façade orientation and time of day meet the following conditions:

* East-facing façades: between 7 a.m. and 11 a.m.

* South-facing façades: between 10 a.m. and 5 pm.

* West-facing façades: between 2 pm. and 10 pm.

This simplified approach assumes that direct solar radiation is likely to enter the indoor space during these periods. Overhangs and surrounding obstructions are not accounted for. Based on these exposure conditions and the shading state, four behavioral categories are defined (Table 4). From these categories, the potential for improvement in heat-mitigation practices with solar shading is then quantified by (eq (4)). Diffuse solar gains, which can also contribute to overheating, are

<table><caption>**Table 3**
Window behavior categories description.</caption><thead><tr><th>Window state</th><th>Temperature conditions</th><th>Behavior name</th><th>Heat mitigation practices</th></tr></thead><tbody><tr><td>Closed</td><td>T<sub>in</sub> ≤ T<sub>out</sub></td><td>Protected</td><td>x</td></tr><tr><td>Open</td><td>T<sub>in</sub> > T<sub>out</sub></td><td>Cooled</td><td>x</td></tr><tr><td>Closed</td><td>{ T<sub>in</sub> > T<sub>out</sub><br>T<sub>in</sub> > 26°C</td><td>Unused<br>cooling</td><td></td></tr><tr><td>Closed</td><td>{ T<sub>in</sub> > T<sub>out</sub><br>T<sub>in</sub> ≤ 26°C</td><td>Neutral</td><td>x</td></tr><tr><td>Open</td><td>T<sub>in</sub> ≤ T<sub>out</sub></td><td>Heating</td><td></td></tr></tbody></table>

<table><caption>**Table 4**
Solar shading behavior categories description.</caption><thead><tr><th>The window is considered exposed to</th><th>Solar shading state</th><th>Behavior name</th><th>Heat mitigation practices</th></tr></thead><tbody><tr><td>Direct solar gains</td><td>Closed</td><td>Blocking direct solar gains</td><td>x</td></tr><tr><td>Direct solar gains</td><td>Open</td><td>Letting direct solar gains</td><td></td></tr><tr><td>No direct solar gains</td><td>Closed</td><td>Neutral closed</td><td>x</td></tr><tr><td>No direct solar gains</td><td>Open</td><td>Neutral open</td><td>x</td></tr></tbody></table>

neglected in this study.

$$Pot_{solar,day} = p_{letting solar gains} / p_{blocking solar gains} \quad (4)$$

#### 3.2.3.3. Combined behavior: window + solar shadings.
Window and shading behaviors are combined both to capture global occupant strategies and to address situations in which window use alone are or are not heat mitigation practices, whereas shading devices mitigate the actual impact. For example, when a window is open while the outdoor temperature is lower than indoors (Cooling category), but the associated solar shading is fully closed, the inflow of cool air is substantially reduced. Such a case should therefore not be interpreted in the same way as an open window and an open solar shading. However, as further discussed in Section 5.3, a limitation of our dataset is that no precise information is available on the actual airflow (e.g., window opening tilt, solar shading opening proportion, or local pressure conditions around the window). To address these uncertainties, two definitions of heat-mitigation practices are applied: a strict definition, which includes only fully optimal combinations of behaviors (Pot<sub>comb strict</sub>), and a tolerant definition, which also accounts for partially mitigated cases (Pot<sub>comb tolerant</sub>), as defined in Table 5 and in eq (3). Two definitions can be seen as the bounds of a range of potential improvement (eq (5)) allowing a more comprehensive assessment of the combined effect of window and shading behaviors on indoor overheating.

<table><caption>**Table 5**
Combined behavior (window and solar shading) description. *Based on sectional questionnaire done in the end of the summer.</caption><thead><tr><th>Window behavior category</th><th>Solar shading behavior category</th><th>Heat mitigated practices (tolerant)</th><th>Heat mitigated practices (strict)</th></tr></thead><tbody><tr><td rowspan="3">Day</td><td>Heating</td><td>Blocking direct solar gains<br>Letting direct solar gains<br>Neutral closed</td><td>x</td></tr><tr><td rowspan="2">Protected</td><td>Neutral open</td><td>x</td></tr><tr><td>Blocking direct solar gains<br>Letting direct solar gains</td><td>x</td></tr><tr><td rowspan="5">Night</td><td rowspan="3">Cooling and</td><td>Neutral closed</td><td>x</td></tr><tr><td>Neutral open</td><td>x</td></tr><tr><td>Open*</td><td>x</td></tr><tr><td>Partially closed*</td><td>x</td></tr><tr><td>Totally closed*</td><td>x</td></tr><tr><td rowspan="3">Neutral</td><td>Open*</td><td>x</td><td>x</td></tr><tr><td>Partially closed*</td><td>x</td><td>x</td></tr><tr><td>Totally closed*</td><td>x</td><td>x</td></tr><tr><td rowspan="3">Unused cooling and Neutral</td><td>Open*</td><td>x</td><td>x</td></tr><tr><td>Partially closed*</td><td>x</td><td>x</td></tr><tr><td>Totally closed*</td><td>x</td><td>x</td></tr></tbody></table>
$$ Pot_{comb} \in [Pot_{comb.tolerant}; Pot_{comb.strict}] \quad (5) $$

Accordingly, two intervals are determined for daytime $Pot_{comb.day}$ and one for nighttime $Pot_{comb.night}$, based on these bounds. The main difference—and the reason for distinguishing between day and night—is that nighttime information is derived from the questionnaire rather than instrumentation.

### 3.2.3.4. Combined behavior: window + fans.
Finally, we examine window behavior when fans are operating, based on the behavioral categories defined in Table 3. This analysis aims to identify whether any consistent combinations of window and fan use emerge.

# 4. Results and discussions

## 4.1. Behavior diversity

Fig. 6 illustrates the wide diversity of occupant behaviors observed across dwellings during the entire experimental campaign. For each dwelling, the daily durations were calculated for each room, then aggregated to produce boxplots. These boxplots show, for each dwelling, the distribution of daily durations of window opening, solar shading closure, and fan usage. The three studied regions are distinguished by color: Southwest (SW) in green, Paris region (IDF) in blue, and South-East (SE) in orange, reflecting different climatic conditions.

Fig. 6 highlights significant diversity in occupant behavior across dwellings. Each white dot represents the mean daily duration for an individual dwelling, while the black line shows the median. For example, average daily window opening durations vary widely—from just over 5 h to more than 20 h. Similarly, solar shading closure duration vary substantially.

This diversity is further illustrated by interquartile ranges, for

example dwelling 2 exhibits very consistent and prolonged window opening (ranging from 18 to 24 h), whereas dwelling 6 shows much more variable behavior (ranging from 1 to 18 h). To sum up, only 14 out of 76 dwellings exhibit interquartile ranges below 10 h, indicating that the vast majority show considerable diversity in occupant behavior across different days and rooms within the same dwelling. This observed diversity aligns with the literature [9,23,35], emphasizing the complexity of occupant behavior.

On average, daily window opening duration is highest in the SE region, with 15.1 h per day, followed by 13.5 h in the SW and 13.2 h in IDF. In contrast, solar shading devices tend to be kept closed during the daytime period (7 a.m. to 10 pm.), with regional average daily closure durations ranging from 9.4 h in SW, 7.5 h in IDF and 10.9 h in SE. This range indicates that some dwellings keep their solar shading almost continuously closed during the day, with closure durations varying between 2 and 14 h. Fan use remains generally low throughout the summer, with regional daily averages below one hour (0.8 h in SE and SW, and 0.4 h in IDF). However, on particularly hot days, fan usage can spike dramatically, sometimes reaching continuous operation for 24 h, as will be discussed in Sections 4.2.3 and 4.4.3.

## 4.2. Indoor and outdoor temperature impact on occupant behavior

Fig. 7 presents the influence of both outdoor temperature (left) and indoor temperature (right) on the proportion of open windows, solar shadings closed, and fans switched on across four moments of the day, and through the entire day (all data) in black.

### 4.2.1. Window effect of temperature

The proportion of open windows increases with outdoor temperature up to a cut-off of approximately 22–28 °C, in average depending on the moments of the day, after which it gradually decreases. This cut-off

<figure><img src="image_7.png" /><figcaption>Fig. 6. Diversity behavior of each dwelling count for each day of each room, solar shading or fan present in a dwelling. Top: Window daily opening time; Middle: Closing time of the solar shading during the 7am-10pm period; Bottom: Fan daily usage time. Each color represents a different region: Green for SW; Blue for IDF; Orange for SE.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 7. Proportion of window open (top), solar shading closed (middle) and fan switched on (bottom) according to moments of the day, outdoor temperature (left) and indoor temperature (right).</figcaption></figure>

temperature is observed at all moments of the day, except at night. During nighttime, the proportion of open windows continues to increase with increasing outdoor temperature, with a steady increase noticeable even beyond 32 °C, however, this increase may reflect less representative behavior due to a limited number of observations at such high nighttime temperatures during the campaign. No significant relationship was found between the proportion of windows open and indoor temperature, except for a slight increase observed above 24 °C.

### 4.2.2. Solar shading – effect of temperature

The proportion of solar shading closed, as a function of indoor and outdoor temperatures, shows an inverse trend to that of window openings. Across the entire day, during the morning and evenings, a U-shaped relationship is observed. Under 22 °C outside, solar shadings are less used. During the afternoon, the higher the outdoor temperature is, the more solar shadings are closed which indicates that occupants tend to use their solar shadings to protect their dwelling from solar gains. In the afternoon, there is a linear increase in the proportion of shading closed with rising outdoor temperature—from 15% of solar shading closed at 15 °C to about 40% at 32 °C. One possible explanation for the afternoon behavior is that occupants actively seek to reduce heat gain by solar radiation; however, this hypothesis does not account for the evening trend. In contrast, in the evening there is a decrease in solar shading closure, with 55% closed at 15 °C, dropping to 40% at 32 °C.

### 4.2.3. Fan – effect of temperature

Fig. 7 (bottom) illustrates that fan use is significantly influenced by both indoor and outdoor temperatures. Fan use begins to increase at an outdoor activation temperature of 20 °C and an indoor activation temperature of 22 °C, regardless of the time of day. It is noteworthy that the

proportion of fans used during nighttime is almost twice as high as during daytime.

## 4.3. Comparison with other datasets

To address RQ2, the observed behaviors were compared to datasets from other countries and contexts described in Table 2. Fig. 8 shows the fitted regression of this study (in black) against other datasets: all days and all rooms combined (left), by moment of day (center), and by room type (right). The logistic regression coefficients obtained from our dataset are provided in Table 6 for future comparisons or for integration into Building Performance Simulation models.

### 4.3.1. Window - behavior across datasets

As noted in Section 4.2.1, our dataset exhibits a cut-off temperature around 22–25 °C. This is consistent with, though slightly lower than, the 28 °C found in Swiss offices [11] and the 25 °C observed in Australian dwellings [19]. In contrast, studies by [12,15] did not capture this cut, as they applied simple logistic regression models without a quadratic term.

In terms of opening proportion, at the cut-off temperature—and more generally between 20 °C and 30 °C—differences between all datasets remain small, ranging between 55% and 70% of windows open.

Below 20 °C, [15] et [19] show a similar decrease, reaching nearly 0% at 10 °C [11]. report a similar trend but with higher opening rates (about 20% at 10 °C). In our study, window opening also decreases below 20 °C, but remains consistently higher than in all other datasets, reaching about 30% at 10 °C—closer to the model of [12]. These differences may be related to variations in habits, climatic contexts, or experimental protocols. One hypothesis is that our dataset only covers
<figure><img src="image_9.png" /><figcaption>Fig. 8. Window, solar shadings and Fans behavior comparison of different dataset from other countries and contexts according to the outdoor temperature.</figcaption></figure>

<table><caption>**Table 6**
Logistic fits coefficients of our database.</caption><thead><tr><th>Type of behavior</th><th>Model name</th><th>a</th><th>b1</th><th>b2</th></tr></thead><tbody><tr><td rowspan="6">Window</td><td>This study - Day</td><td>-2.995</td><td>0.27</td><td>-0.005</td></tr><tr><td>This study - Night</td><td>-2.05</td><td>0.15</td><td>-0.001</td></tr><tr><td>This study - Bedroom</td><td>-2.572</td><td>0.248</td><td>-0.005</td></tr><tr><td>This study - Living room</td><td>-4.741</td><td>0.431</td><td>-0.009</td></tr><tr><td>This study - Other</td><td>-3.345</td><td>0.303</td><td>-0.006</td></tr><tr><td>This study</td><td>-3.295</td><td>0.307</td><td>-0.006</td></tr><tr><td rowspan="6">Solar shading</td><td>This study - Day</td><td>1.122</td><td>-0.138</td><td>0.004</td></tr><tr><td>This study - Night</td><td>1.854</td><td>-0.118</td><td>0.004</td></tr><tr><td>This study - Bedroom</td><td>2.942</td><td>-0.249</td><td>0.006</td></tr><tr><td>This study - Living room</td><td>2.197</td><td>-0.234</td><td>0.006</td></tr><tr><td>This study - Other</td><td>2.562</td><td>-0.286</td><td>0.007</td></tr><tr><td>This study</td><td>2.564</td><td>-0.241</td><td>0.006</td></tr><tr><td rowspan="3">Fan</td><td>This study - Day</td><td>-9.846</td><td>0.382</td><td>-0.005</td></tr><tr><td>This study - Night</td><td>-11.581</td><td>0.571</td><td>-0.009</td></tr><tr><td>This study</td><td>-9.685</td><td>0.425</td><td>-0.007</td></tr></tbody></table>

the summer period (June 1 to September 30), whereas [15] et [19] collected year-round data. This limitation may restrict the generalizability of our models to mid-season or winter conditions and raises the question of whether data from different seasons should be combined.

For the time-of-day analysis for comparison purposes, we adopted the classification used by [19]—daytime (6 a.m.–6 pm.) and nighttime (6 pm. –6 a.m.)—rather than that of Fig. 7. During daytime, our dataset

and that of [19] both show a cut-off temperature around 25 °C, with similar opening proportions at that threshold. At night, however, no cut-off is observed in our data, unlike in [19]. This may be due to differences in building systems: [19] studied mixed-mode dwellings (natural ventilation and A/C), where occupants tended to close windows and use air conditioning above a certain temperature at night. In contrast, in our study with buildings without A/C, keeping windows open at night may be the only way to cool indoor spaces after a hot day.

Regarding room type, [18] reported lower opening proportion in bedrooms than in living rooms. In our dataset, the window opening proportion showed no significant variation between room types as a function of outdoor temperature. Above 20–25 °C, our results are closer in shape and proportion to [18] living-rooms data. However, our models consistently predict about 20 % more windows open than [18]'s bedroom data. This gap might due from cultural differences, but more likely from the presence of air conditioning in the [18] buildings, which could reduce occupants' reliance on natural ventilation for cooling.

### 4.3.2. Solar shading - behavior across datasets

The comparison of solar shading behavior should be interpreted with caution, as our logistic fit only reflects solar shadings use between 7 a.m. and 10 pm. (see Section 3.1.4). It should be noted that the other studies focused on office buildings. The patterns reported by [12] and [26] differ both in temperature dependence and in the overall proportion of closed shading devices. Our results can be divided into two distinct
ranges: from 10 °C to 25 °C, opening proportions are consistent with those observed by [26], while from 25 °C to 35 °C, they align more closely with [12], showing an increasing proportion of closed solar shading as outdoor temperature rises.

### 4.3.3. Fan - behavior across datasets

The observed trends for fan use align across multiple datasets ([12, 15, 19], p. 201; [26,33]), all showing increased fan use with rising outdoor temperatures. However, the proportion of fan use in our dataset is considerably lower—around 5% at 25°C compared to approximately 15% in [19] Australian residential dataset. Notably, [26] reports fan usage in French offices at about 20% at 25°C, which, although higher than our residential data, but remains the smallest proportion compared to other European countries, in his study. This suggests that cultural factors may influence fan usage; fans are relatively uncommon in metropolitan France, and fan use is a more recent practice. Additionally, differences in instrumentation and data collection methods, as discussed in Section 3.1.3, might also explain variations between our study and others.

Regarding the outdoor activation temperature—the outdoor temperature at which fans start being used—our dataset indicates 20 °C, consistent with other studies, although some datasets, such as [15], report a slightly higher activation temperature of 22 °C.

Finally, fan use is higher at night than during the day, consistent with

observations in Australian homes [19], though the proportions differ significantly, as previously mentioned.

### 4.4. Changes in regional daily occupant behavior during heatwaves and typical summer periods

This section proposes to study the mean regional daily profile of window, solar shading and fan use according to the 3 regions studied, summarized in Fig. 9. Specifically, the hourly states of all windows of all dwellings within each region are averaged to determine these daily profiles. Classical summer periods (all data available except the selected heatwave period) are compared with heatwave periods, as defined in Section 2.2.2, to investigate changes in daily behavioral patterns and identify potential regional differences.

#### 4.4.1. Window – changes in daily occupant behavior

In Fig. 9, clear distinction is observed between the typical summer period and the heatwave period. In both period, nighttime opening behaviors are adopted: windows are closed during the day and opened at night by the occupants. However, this phenomenon is particularly exacerbated during the heatwave, especially noticeable in the SE and SW regions, where the difference of window open proportion between night and day is about 20% and more than 25%, respectively. It seems

<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Regional mean daily profile of behaviors during the classical summer and a heatwave period.</figcaption></figure>
that there are fewer “purely” nocturnal opening strategies in the Paris region (IDF), as the difference of proportion between night and day is smaller (~13 %). Although, in the Paris region, a peak in window openings during the morning is observed. We can conclude that for part of the population, there is a change of behavior between the classical summer period and the heatwave with a strategy of heat protection by closing the window when temperatures are high (during the day) and

cooling the dwelling by opening the window when temperatures are lower (at night).

### 4.4.2. Solar shading – changes in daily occupant behavior

Fig. 9 shows that there is a higher proportion of solar shading closed during the heatwave period compared to the typical summer period. Regarding the difference between the regions, more solar shadings are

<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Proportion of categorized behaviors observed in the dataset during a heatwave. a. Window behavior. b. Solar shading when $T_{in}<T_{out}$ and between 7 a.m. to 9 pm. for a conservative lux threshold of 100 lx. c. Combined behavior.</figcaption></figure>
closed in the Southeast, which experiences warmer conditions both outdoor and indoor and more intensive solar radiation. During the heatwave period, the state of closed shadings is higher in the SW. This may be explained by the fact that all dwellings in this region are insulated, and therefore more sensible to solar gains, or alternatively by differences in occupants' shading habits.

In all three regions, an increase in the proportion of closed solar shading is observed during the early morning and late evening. This phenomenon likely reflects real occupant behavior, as people tend to close solar shading at night for privacy, to limit external light or by habits. Nevertheless, it may be exaggerated by the assumption made to consider the shading closed based on the measurements: as we assume a threshold of 100 lx under which the shading system is considered totally closed, this could result in an over-estimation of shadings closed during morning and late evenings when the outdoor ambient light lowers.

In addition, to gain insights into occupants' habits regarding external solar shading at night, survey responses were analyzed. According to the survey, 66% of dwellings keep their solar shadings open or partially open during the night in the classical summer period, and the proportion increases to 79% during heatwave periods. According to the survey, the unanimous reason for keeping solar shading closed at night is to avoid light entering the rooms. Conversely, the main reasons occupants open their solar shading at night are to allow fresh air into the dwelling (83%).

The survey and monitoring results indicate an adaptive strategy in some rooms during the heatwave: solar shading is closed during the day to block solar gains and opened at night alongside windows to maximize cooling. This highlights the need to study window and shading behaviors in combination to fully understand occupant strategies, as further analyzed in Section 4.5. It should also be noted that in some dwellings, solar shading may be partially or totally closed in the late evening or opened in the early morning, however it is hard to quantify it based on the measurements.

### 4.4.3. Fan – changes in daily occupant behavior

First, a significant increase in fan usage is observed during the heatwave period compared to the typical summer period, confirming the dependence of fan use on outdoor and indoor temperatures, as observed in the previous Section 4.2. During heatwave, fan use is notably higher after 1 pm., continuing until the following morning around 8 a.m., likely until occupants wake up. Interestingly, fan use is higher in the SW region during the afternoon (1 pm – 11 pm.) and lower during the nighttime compared to the other regions. This difference might be explained by the higher humidity levels observed in this region.

## 4.5. Potential for improvement in occupant heat-mitigation practices during heatwave

### 4.5.1. Window and solar shading - potential for improvement

To address RQ1 and RQ3 and quantify the potential for improvement in occupant heat-mitigation practices, behaviors are categorized as described in Section 3.2.3. Fig. 10 presents the distribution of these behavioral categories across the entire dataset during the heatwave period, as the trends are roughly similar between regions ($Pot_{window}$ during heatwave SW: 33%; IDF: 41%; SE: 40%) and consistent with regional differences done in of Section 4.4.

**Window behavior.** As shown in Fig. 10a, the potential for improvement in heat mitigation practices with window ($Pot_{window}$), defined as the proportion behaviors that are not heat mitigation practices (described in Table 3), is 40%. This includes 26% of heating behaviors and 14% of unused cooling behaviors. In contrast, thermally optimal behavior, so called heat-mitigation practices, accounted for 60% of all observed behaviors during heatwaves, comprising 21% classified as protected and 36% as cooling. Overall, occupants adopted more heat-mitigation than suboptimal behaviors.

To complement the figure, a closer examination of day versus night behaviors during heatwaves reveals clear differences in the potential for improvement. At night, when outdoor temperatures are lower than indoors (cooling and unused cooling behaviors), three-quarters of behaviors (68%) are heat-mitigation practices. By contrast, during the day fewer than half of behaviors (46%) are heat mitigation practices. This indicates a greater potential for improvement in daytime practices, which may reflect limited occupant awareness of the benefits of keeping windows closed during hot daytime conditions or a misperception of the impact. This observation is consistent with responses to an open-ended question in the survey, which suggest that some occupants confuse ventilating the dwelling with cooling the building, not realizing that allowing hot outdoor air inside can actually increase indoor temperatures and heat the building's thermal mass. Even if air movement creates a sensation of cooling, a fan could achieve a similar effect without raising the indoor temperature.

**Solar shading behavior.** Fig. 10b shows that the potential for improvement in solar shading use during the day ($Pot_{solar,day}$) was 43%. Meaning that, among the 37% of daytime periods when solar shading should be closed to reduce solar gains, they were not fully closed only 43% of the time. In periods without significant solar gains, solar shadings were nevertheless closed in about half of the cases (49%).

When applying a less conservative threshold of 500 lx (instead of 100 lx threshold, described in Section 3.1.4) to account for situations where solar shading are partially closed $Pot_{solar,day}$ decreases to 31%.

**Comparison and combined behaviors.** Fig. 10c shows the combination of window and solar shading behaviors. These results suggest a greater potential for improvement in heat mitigation practices with windows and especially during daytime. As described in Section 3.2.3.3, to quantify the potential for improvement in heat mitigation practices, an interval is determined, see Table 7, based on two assumptions regarding how strict we define the notion of heat mitigation practices.

The tolerant potential for improvement is approximately half the stricter one for both the night and the day. During the daytime, this difference is explained by the high prevalence of the combination of closed solar shading combined with open windows when it is hotter outside than inside, and the relatively limited proportion of the combination of letting solar gains and protected. At night, differences arise from how “partially open” and “closed” solar shading is defined, which affects how much cool air can enter the dwelling. Despite these uncertainties, providing even such a range of potential for improvement allows to demonstrate the significant potential to improve occupant behavior to reduce indoor overheating.

As shown in Fig. 11, both the potential for improvement and the observed behaviors vary across dwellings. For simplicity, this section presents an analysis of $Pot_{window}$, and not of $Pot_{comb}$, given that, as discussed previously, combining window and shading behaviors introduces considerable uncertainty regarding what constitutes a heat-mitigation practice.

No dwelling exhibited thermally ideal behavior (0% potential for improvement) across all rooms during the entire heatwave, illustrating the difficulty of maintaining optimal behaviors consistently.

* The dwelling with the lowest potential for improvement had a $Pot_{window}$ of approximately 15%.

* Conversely, the dwelling with the highest potential reached 75%.

* About 21% of dwellings had a potential for improvement below 30%, while 19% exceeded 50%.

<caption>**Table 7**</caption>

Potential for improvement in heat mitigation practices considering combined behavior (window and solar shading) for nighttime and daytime, under both tolerant and strict definitions of heat-mitigation practices.

<table><thead><tr><th></th><th>tolerant</th><th>strict</th></tr></thead><tbody><tr><td>day</td><td>28%</td><td>64%</td></tr><tr><td>night</td><td>35%</td><td>76%</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>**Fig. 11.** Proportion of combined behavior per dwelling during a heatwave, ordered by potential of improvement of heat mitigation practices with window (*Pot*<sub>window</sub>).</figcaption></figure>

Fig. 11 also illustrates a diversity of behavior among dwellings. For example, dwelling 45, with a potential for improvement of roughly 40%, consistently exploits natural cooling at night (*cooling*), but rarely closes windows during the day when outdoor temperatures exceed indoor temperatures (*heating*). In contrast, dwelling 58, with a potential of 50%, keeps windows closed most of the time and consistently closes solar shading, however limiting opportunities for cooling at night.

Furthermore, Fig. 11 provides an average over both time (over a heatwave week) and space (all rooms) for each dwelling, but it does not reveal finer-scale differences. Examining in more details two dwellings with a low potential for improvement (< 20%) illustrates this point. Dwelling 4 exhibits mostly heat-mitigation behaviors consistently in the bedroom and kitchen, but occasionally suboptimal behaviors in the living room. In contrast, dwelling 67 shows more uniform behavior across rooms, with suboptimal behavior occurring simultaneously, likely due to a practical constraint or oversight.

### 4.5.2. Potential for improvement - window and fan

During the heatwave, 31 dwellings used fans, with highly variable patterns and durations of use. Overall, windows were open during 69% of the time when fans were on. The proportion of each category of categorized window behaviors when fans are used is analyzed. The general proportions are that 43% of fan use occurred when window behavior is *cooling*, consistent with the findings of Section 4.4.3 showing increased fan operation in the evening and at night, when indoor temperature is higher than outdoor. In parallel, 26% of fan use corresponded to *heating*, 21% to *protected*, 9% to *unused cooling*, and less than 1% to *neutral*.

Surprisingly, *unused cooling* behavior proportion—when fans were used while windows remained closed despite cooler outdoor conditions—accounted for only 8%. One might expect occupants to rely more on fans in combination with this window behavior to compensate for limited natural ventilation (due to imaginable constraints, mosquitoes etc.), but it remained marginal: no dwelling used it extensively, with a maximum of 28% and only four exceeding 15%. This indicates that fans were generally perceived as a complement to natural

ventilation rather than as a systematic substitute when window opening is limited (e.g., noise, insects, security).

At the dwelling scale, only 18 households systematically combined fan and window behaviors (i.e., more than 50% of their fan use fell into one window behavior category): 9 used fans mostly in *cooling*, 5 in *heating*, and 4 in *protected* situations. The remaining 13 households exhibited no uniform pattern across categorized window behaviors, suggesting that fan use was more strongly influenced by individual preferences, habits, or contextual constraints than by a consistent thermal strategy.

## 5. Discussion

### 5.1. Objectives and limitations of the database

This study relied on the largest French database of occupant behaviors collected to date, providing an unprecedented opportunity to study summer and heatwave behaviors. Nevertheless, several biases and limitations need to be acknowledged. First, the sample is not representative of the French population and the national building stock. The study focused exclusively on multi-family housing, with social housing being slightly overrepresented (40 % in the dataset vs. 30 % nationally [32]). The share of smokers (24 % vs. 30 %; [36]) and retirees (25 % vs. 25 %) was broadly comparable to the French population. However, socio-economic categories were biased: managers and intellectual professions (47 % vs. 19 %) and employees (41 % vs. 27 %) were overrepresented, while manual and intermediate workers (6 % vs. 45 %) were underrepresented [16]. Second, as explained in Sections 3.1.2 and 3.1.4, the instrumentation did not allow for finer temporal resolution than daily occupancy nor for capturing solar shading positions at night.

### 5.2. Insights on occupant behaviors and comparison with other datasets

The analysis of the monitoring campaign reveals substantial diversity in occupant behaviors, varying with indoor-outdoor temperature, time of day, and dwelling characteristics. Some dwellings
maintained windows open or closed almost continuously, while others exhibited highly variable patterns [4]. Despite this heterogeneity, general trends emerged: windows were more often closed during hot daytime conditions and opened more frequently at night.

Fan use increased with outdoor temperature, with activation temperature around 20 °C. However, overall usage was relatively low (at least three times less) in French dwellings compared to other countries. This may be explained partly by cultural differences, as fans are only recently used in metropolitan France and partly by the limitations of our instrumentation.

For solar shadings, occupants tend to close them less below 22 °C outside, increasing in the afternoon as temperatures rose.

For windows, a cut-off temperature between 22 and 25 °C was observed, slightly lower than thresholds in other studies (28 °C in Swiss offices; 25 °C in Australian dwellings). Across datasets, window-opening proportions in this range were similar (55–70 %). Unlike [18], we observed no strong differences between living rooms and bedrooms, consistent with [41], see Section 4.3.

Finally, the absence of air conditioning explains the lack of nighttime cut-off temperature during warm periods. In buildings with AC, windows are typically closed when cooling is activated. This suggests that introducing AC in free-running French buildings would likely reduce natural night cooling and increase reliance on mechanical cooling. If AC is implemented, promoting night cooling through window opening—particularly in rooms other than bedrooms—should be prioritized before mechanical cooling.

### 5.3. Potential of improvement in heat mitigation practices during heatwave

During heatwave, occupants change their behavior and tend to engage in heat mitigation practices to protect the dwelling from overheating. To sum up it has been observed:

* increased night window opening to cool down the dwelling and closed window during the day,

* greater proportion of shading closed during the day to avoid solar gains, open at night,

* higher fan use, particularly in the evening and at night, often combined with window opening, and

* combination of several of these behaviors at the same time.

To determine the proportion of improvement in heat mitigation practices, occupant behaviors were categorized according to the indoor-outdoor temperature difference. We can conclude that the behaviors observed were far from thermally optimal. Even in the best-performing dwellings, an estimated 15 % potential for improvement remained, while in the least-performing dwelling the potential reached 75 %. This highlights both the potential for improvement and the difficulty of reaching an “ideal” or perfect behavior thermically speaking. Occupants face constraints and trade-offs—practical, social…—that prevent systematic adoption of best heat mitigation practices, even when aware of them. This suggest the need to reinforce public awareness campaigns, complemented by other measures such as the development of coaching devices that can guide and nudge occupant behavior toward more effective practices [17,21]. However, the extent to which such campaigns can improve behaviors remains uncertain, given both their variable effectiveness and the constraints faced by occupants. Yet, designing buildings under the assumption of systematic suboptimal behaviors could make comfort standards unattainable without mechanical cooling, encouraging widespread AC adoption—an outcome that is environmentally undesirable. This dilemma raises a key question for future occupant modeling efforts as well as for upcoming regulations: to what extent should suboptimal behaviors be incorporated, in order to strike a balance between realism—by accounting for occupants’ constraints—while avoiding conclusions that could inadvertently

encourage the spread of AC?

The combination of different adaptive behaviors is frequently discussed in the literature and often described as the next step in occupant behavior modeling [20]. However, it remains relatively underexplored. One novelty of our study is to try to answer this gap, with window-solar shading and window-fan interactions, during a heatwave. This is particularly relevant, as these combinations can strongly influence indoor overheating. Yet, several limitations and challenges arise from our work.

First, despite relying on a relatively large dataset, the analysis of combined behaviors remains limited. As already highlighted in the literature [5], even large occupant behavior datasets quickly become fragmented, and especially when focusing on short heatwave periods (seven days in our case) and on multiple behavioral categories. This inevitably raises questions about the representativeness of the results.

Second, the combination of behavior directly affects airflow rates, that are known to have a substantial impact on overheating dynamics [37]. However, our database—as the vast majority behavior-monitoring databases—lacks detailed information on window opening angles or tilt positions, as well as solar shading positions, and information about local wind. This strongly limits the ability to estimate the actual airflow through dwellings and the actual cooling and heating potential of window. Future monitoring campaigns should therefore aim to capture window and shading positions with greater precision and attempt to quantify airflow more directly. Yet, achieving this remains challenging, and to the authors’ knowledge, no reliable low-cost and non-intrusive methods currently exist. Nevertheless, it is important to emphasize the novelty and effort of this monitoring campaign and analysis, which, for the first time to our knowledge, attempted to investigate solar shading positions in relation to solar heat gains, as well as combined behaviors affecting airflow reduction—a parameter that remains rarely studied in the literature due to the inherent difficulty of measuring it in practice.

Third, spatially combined behaviors—such as the simultaneous implementation of different strategies in multiple rooms of the same dwelling—were observed but are beyond the scope of this article. Nevertheless, future work should definitely address this aspect, as spatial combinations—like cross-ventilation achieved by opening doors and windows on differently oriented façades—could substantially enhance the building’s cooling potential.

Fourth, the separate impact of occupant behavior on overheating is dependent on dwelling typology (insulation level, thermal inertia, layout, etc.), but remains insufficiently studied. Consequently, the combined impact on overheating of behaviors is also likely to be highly dependent on these dwelling characteristics. Future studies should evaluate how combined occupant behaviors affect overheating across different weather conditions and dwelling typologies and use overheating metrics to determine a weighted potential for improvement, identifying which behaviors should be prioritized or avoided for each dwelling type. At this stage, however, and given the large uncertainties surroundings airflow estimation the potential for improvement as defined in our study—and the range we propose—represents a solid first step and the best achievable compromise with the available data.

### 5.4. Implications for modeling and overheating risk assessment

Our findings underline the need for a realistic occupant behavior models that incorporate diversity, in order to more accurately represent actual occupant behavior in building performance simulations. Assuming idealized single occupant behavior model, as done in French regulation and most regulations, does not adequately represent real occupant behavior [3,28]. Such simplifications smooth out day-to-day and inter-dwelling diversity [27] leading to misleading “average” behaviors that fail to capture extreme cases. In the context of heatwaves, this can even be dangerous, as the “average occupant” is not necessarily the most vulnerable one. Instead, risks are concentrated among those whose behaviors—or constraints—place them at the extreme. Ignoring
this diversity risks underestimating overheating health impacts and
misguiding the construction and retrofit of buildings.

A possible solution to ensure that buildings remain reasonably comfortable is to adopt modeling approaches that do not average occupant behaviors or consider only one behavior but instead incorporate behavioral diversity. Such models should account for reasonably suboptimal practices in order to remain realistic, while still being optimistic about the potential for behavioral change (e.g., thanks to future awareness campaigns or other interventions). By doing so, they can better assess building resilience and guide the design of strategies that maintain thermal comfort under different behavioral scenarios.

To achieve this, stochastic models that integrate behavioral diversity between dwellings, such as those proposed by [9,35], appear to be a promising solution. Our results show that simple logistic regressions based solely on indoor or outdoor temperature are insufficient to capture window-opening or solar-shading behaviors during summer. While quadratic terms can capture cut-off temperatures, temperature alone—whether indoor or outdoor—is not enough to model the complexity of occupant behavior. Our results reveal also that the time of day is also a critical factor, as 25 °C at night versus during the afternoon implies very different risks and behaviors. Incorporating both temperature (indoor, outdoor, or both) and the hour of the day can help model the observed change of behavior between typical summer conditions and heatwave periods.

Another approach, which can incorporate diversity and account for these various elements, is to characterize behavior for each day, each room, and each dwelling. For example, clustering daily profiles—as done by [4]—and then predicting these clusters provides a way to model and simulate diversity.

In short, modeling occupant behavior based on this dataset repre-
sents the next step in research.

While our study primarily examines occupant behavior concerning overheating and the resulting thermal comfort issues, the insights gained on occupant behavior and future models could also contribute to indoor air quality assessment and support a more comprehensive approach to comfort evaluation in buildings.

6. Conclusion

This study provides new insights into occupant behaviors during summer and heatwaves in French free-running dwellings, based on the largest dataset of its kind. Our results show that the use of windows, solar shadings, and fans varies greatly across dwellings and through summer. While general trends broadly align with behaviors observed in other countries and contexts, notable differences emerged, highlighting specific characteristics of this context—such as the limited use of fans. By categorizing and combining behaviors, this study highlights both the potential for improving heat-mitigation practices and the difficulty of achieving consistently thermically “ideal” behaviors due to practical constraints of the occupants.

These results have two main implications. First, they highlight the
need to support occupants in adopting more effective heat-mitigation
practices, for instance through targeted information campaigns or
technological solutions to nudge behavior such as smart thermostats.
Some heat mitigation practices during heatwave are less commonly
adopted than others, such as closing windows during the day and the use
of fans, especially when practical constraints limit natural cooling. It
suggests that these behaviors should be a particular focus of awareness
campaigns. Second, they call for the development of occupant behavior
models tailored to French free-running residential buildings, capable of
capturing inter-dwelling diversity, temporal dynamics (time of the day,
and change of behavior during heatwave), and combined behaviors such
as window operation and solar shading.

It is necessary to keep it mind that the database is not representative
of the French population and building stock. Nevertheless, the strengths
of this work lie in its unique scale of data, the determination of potential

improvements, and its focus on the combination of behaviors. This
approach remains rare in the literature and provides a solid foundation
for the future development of scenarios and stochastic models. Such
occupant behavior models would allow thermal comfort and resilience
to be assessed under diverse, and sometimes suboptimal, occupant be-
haviors, providing a more accurate representation of real-world be-
haviors and supporting the design of more comfortable buildings.

Data availability

The dataset analyzed in this study contains personal and sensitive information subject to General Data Protection Regulation (GDPR) and is not publicly available. After further processing and anonymization in compliance with the privacy regulations, fully anonymized data will be released via a public repository.

Declaration of generative AI in the writing process

During the preparation of this work the authors used GPT-5 mini to
check grammar, suggest rephrasing and translation. After using this
tool/service, the authors reviewed and edited the content as needed and
take full responsibility for the content of the published article.

CRediT author statement

**Kevin Campagna:** Conceptualization, Methodology, Software, Data Curation, Validation, Formal analysis, Investigation, Writing - Original Draft **Anaïs Machard:** Conceptualization, Methodology, Validation, Funding acquisition, Writing - Review & Editing **Aurélie Fouquier:** Conceptualization, Methodology, Validation, Writing - Review & Editing **Dorothée Charlier:** Conceptualization, Methodology, Writing - Review & Editing **Monika Woloszyn:** Conceptualization, Methodology, Funding acquisition, Writing - Review & Editing

CRediT authorship contribution statement

**Kevin Campagna:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Anaïs Machard:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. **Aurélie Fouquier:** Writing – original draft, Validation, Supervision, Methodology, Conceptualization. **Dorothée Charlier:** Writing – review & editing, Methodology, Conceptualization. **Monika Woloszyn:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no conflict of interest related to
this study.

Acknowledgment

This work was supported by a French government grant managed by
the Agence Nationale de la Recherche under the Investissements
d'Avenir program (ref. ANR-18-EURE-0016 - Solar Academy). The au-
thors would like to thank all occupants participating in this database
RENOPTIM PROFEEL CEE project. The authors also would thank all the
persons who contribute to the data collection and curation: Hugo Cou-
landreau, Pierrick Gervasi, Elodie Said, Thierry Guiot, Fanny Arrayet.

Data availability

The dataset analyzed in this study contains personal and sensitive information subject to General Data Protection Regulation (GDPR) and
is not publicly available.

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