# Observed vs. simulated occupant behavior: Exploring the summer comfort gap induced by occupants in the French building energy code

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

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Kevin Campagna, Anaïs Machard, Aurélie Foucquier, Dorothée Charlier, Monika Woloszyn. Observed vs. simulated occupant behavior: Exploring the summer comfort gap induced by occupants in the French building energy code. Journal of Physics: Conference Series, 2025, CISBAT 2025 - The Built Environment in Transition Hybrid International Scientific Conference, 3140, pp.112001. 10.1088/1742-6596/3140/11/112001. cea-05513806

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# Observed vs. simulated occupant behavior: Exploring the summer comfort gap induced by occupants in the French building energy code

K Campagna<sup>1,2,3</sup>, A Fouquier<sup>3</sup>, A Machard<sup>2</sup>, D Charlier<sup>4</sup>, M Woloszyn<sup>1</sup>

<sup>1</sup> Université Savoie Mont-Blanc, CNRS, LOCIE, 73000 Chambéry, France

<sup>2</sup> Centre Scientifique et Technique du Bâtiment (CSTB), 24 Rue Joseph Fourier, 38400 SaintMartin-d'Hères, France

<sup>3</sup> Univ. Grenoble Alpes, CEA, Liten, INES, 73375 Le Bourget-du-Lac, France

<sup>4</sup>Savoie Mont-Blanc, IREGE, 4 chemin de Bellevue, 74940 Annecy-le-Vieux, France
[kevin.campagna@cstb.fr](mailto:kevin.campagna@cstb.fr)

**Abstract.** This study examines the realism of the window and solar shading operation model in the French building energy code and the resulting summer comfort gap. It compares data from an experimental campaign conducted in 58 dwellings across three French regions with simulations performed using Cometh RE2020, the Building Performance Simulation (BPS) software of the French building energy code. The findings highlight significant discrepancies between regulatory models and actual occupant behavior. While the regulatory framework captures general trends, it fails to account for the variability in behaviors across different dwellings and rooms. Notably, the regulatory model assumes widespread nighttime window opening during heatwaves, whereas this behavior is observed only in certain dwellings and specific rooms within those dwellings. These differences lead to a notable comfort gap, with 16 out of the 58 dwellings being less comfortable than predicted. The study suggests two main solutions to reduce the summer comfort gap: improving occupant behavior models in regulations and supporting occupants in adopting more thermally efficient practices.

## 1. Introduction

France experiences predominantly continental and Mediterranean climates, which has historically led to a focus on reducing heating energy consumption in buildings. This emphasis is reflected in the first four building energy codes, which evolved after the 1973 oil crisis to impose a reduction in energy consumption. However, summer thermal comfort was only addressed in 2000 with the introduction of an indoor temperature threshold. This concern grew with increasing awareness of climate change and its impact on heatwave intensity and frequency. Subsequent regulations, including RT2005, RT2012, and RE2020, progressively emphasized bioclimatic design, natural ventilation, and finally introduce a degree-hour (DH) threshold to assess summer comfort of buildings [1].

A recent international review of building codes [2] highlighted that occupant representation remains simplistic across regulations, typically scenario-based rather than rule-based. France's approach is considered relatively advanced, yet sometimes « quite optimistic » [2] in modeling occupant behaviors. Given that occupant interactions with windows and shading devices during summer significantly influence indoor overheating and thus occupant comfort, occupant modeling assumptions strongly affect simulation outcomes [3], [4]. This leads to the two following research questions:
**RQ1:** How far from reality the French building regulation behavior with window and solar shading is far from reality? – *evaluate the French regulation behavior model realism*

**RQ2:** What is the impact of these difference on comfort? - *measuring the comfort gap*

The goal of this study is not too harshly criticize the behavior model used in the French building regulation but rather to identify its limitations and determine which aspects are not accounted for. By doing so, we aim to propose potential improvements to enhance its accuracy and minimize the comfort gap it may induce. This raises questions about the applicability and purpose of regulatory simulations. To achieve this, the study compares window and solar shading interactions behaviors in 58 instrumented residential buildings in France during the summer of 2023 with simulations of the same buildings based on regulatory assumptions. It also assesses differences in indoor comfort by analyzing their respective overheating levels.

## 2. Methodology

### 2.1. General methodology

The general methodology aims to evaluate the French regulatory behavior model realism and to determine the induced comfort gap. This research uses a dataset composed of 76 French instrumented dwellings during the 2023 summer, already used and described in Campagna et al (2024) [5], [6]. To compare the observed behavior with that predicted by the French regulatory behavior model, only 58 of the monitored dwellings were simulated due to the availability of necessary information for building simulation (including floor plans and data from the Energy Performance Certificate) and appropriate weather files. The simulations were conducted using the Building Performance Simulation (BPS) software Cometh RE2020, which is mandated by the French building energy code and legally required for new constructions. These simulations are necessary for the determination of the window and solar shading states of the regulatory behavior model as well as the indoor temperature of the dwellings. Once the simulations are completed, the simulated behaviors for window operation and solar shading are compared with the observed behaviors. To determine the comfort gap between simulated and observed data, the indoor comfort is compared throughout the entire summer using the Degree-Hour (DH) indicator. The DH (Degree-Hours) indicator represents, over the course of a year, the sum of the number of hours considered as uncomfortable, with each hour weighted by the temperature difference (in °C) between the indoor temperature and the comfort threshold: 26°C at night (fixed threshold) and between 26°C and 28°C during the day (variable threshold, accounting adaptative comfort).

### 2.2. The 58 dwellings

#### 2.2.1. *The instrumented dwellings.* The dataset used in this study was collected within the RENOPTIM project, led by the Centre Scientifique et Technique du Bâtiment (CSTB) and the Union Sociale pour l'Habitat (USH), with the goal of reducing air conditioning use in French dwellings by identifying key factors contributing to indoor overheating. Fifty-eight apartments across three different French regions were monitored from June to September 2023, collecting data on indoor and outdoor conditions, window and solar shading states, as well as occupant and building characteristics. The dwellings varied in thermal performance, floor position and orientation ensuring diversity in exposure. Half of the residences had roof-mounted weather stations, while others were linked to the nearest available station. One heatwave period of 7 days for each region were determined according to Ouzeau method [7]. Data processing included removing inconsistent sensor data, removing long periods of absence, and resampling all data to a uniform 10-minute time step. Luminosity sensors were installed on windows to monitor the state of solar shading. As a result, solar shading states could not be recorded at night (10pm to 6am), and luminosity values below 100 lux were considered indicative of closed solar shading.

#### 2.2.2. *The simulated dwellings.* The same 58 dwellings were simulated using the French building energy code BPS software. It should be noted that there are some biases in our simulations compared to those
conducted by engineering firms: the noise exposure level is considered equal for each window of a dwelling, and weather file use the weather file do not consider the possible urban heat island. In the simulations the window and solar shading states are output as ratios. To compare with the observed data (where only the state of the windows and shading systems are known, not their opening ratio), it was assumed in the output simulation that a window is considered open if its opening ratio exceeds 5%, and a solar protection is considered closed if its closing ratio is above 50%.

## 2.3. Brief description of the behavior model in the French building energy code

### 2.3.1. Window operation model.
The regulatory model has two modes for window operation: an automatic and a manual mode. Since the focus of the study is on occupant behavior, the manual window operation model is used. In this model, the opening ratio of each window is proportional to five variables: the occupancy ratio, the proportion of the window in the thermal zone, a moderation coefficient to account for opening constraints (noise and security concerns), a moderation coefficient to account for outdoor temperature, and coefficient to account for the indoor operative temperature.

### 2.3.2. Solar shading operation model.
As for the window operation model, the manual mode is selected. The solar shading operation model is even more complex than the window model, but its closing ratio depends on factors such as: day/night distinction, global incident illuminance, average operative temperature, occupancy, wind speed, and season.

# 3. Results

## 3.1. Behavior according to indoor and outdoor conditions

Figure 1 compares the proportion of open windows and closed solar shading observed during the measurement campaign (right) with those from French building energy code simulations (left) for the same buildings under the same weather conditions.

<figure><img src="image_2.png" /><figcaption>Figure 1: Comparison of the simulated behavior of the French building energy code) and the observed behavior during an experimental campaign with windows and solar shading according to indoor and outdoor temperature</figcaption></figure>

### 3.1.1. Temperature influence – Window operation.
The regulatory behaviors have wider variation in the proportion of open windows depending on indoor and outdoor temperatures than the observed window operation behavior. Although the observed behaviors show less variation, they follow the same trend regarding cut-off temperatures for outdoor temperature, around 20°C and 25°C. This cut-off temperature corresponds to the point where the increase in the proportion of open windows stops, followed by a decline as outdoor temperature rises. This phenomenon has also been observed in other datasets [8], [9], [10]. Analyzing indoor temperature is more complex, as it is partially influenced by the window state. However, in the simulated behaviors, a cut-off indoor temperature is also observed. This threshold, around 30°C, is higher than the one identified for outdoor temperature, and the proportion of open
windows at this point is significantly greater. This trend is not observed in the behaviors recorded during
the experimental campaign.

3.1.2. *Temperature influence – Solar shading operation.* As with window behaviors, the variation in the proportion of solar shading closed with respect to indoor and outdoor temperatures is much more pronounced for the simulated regulatory behaviors. In these cases, a linear increase in shading closure is observed starting at 20°C, with similar proportions for both indoor and outdoor temperatures. For the observed behaviors, however, a different trend is seen, there is a decrease in closed solar shading up to around 25°C, followed by an increase beyond this temperature.

3.1.3. *Temperature influence – key findings.* Regulatory behaviors show wider variation in the proportion of open windows or closed shutters for different temperature. A similar cut-off temperature trend around 20°C of outdoor temperature is observed, where the proportion of open windows decreases as the temperature rises, both in the observed and regulatory behaviors, suggesting a certain realism in the occupant modeling within the regulation. However, no similar trends are found for the proportion of open windows based on indoor temperature, nor for the proportion of closed solar shading based on indoor and outdoor temperatures.

3.2. *Mean daily behavior profile with window and solar shading during heatwave*

Figure 2 shows the daily mean proportion of window open and solar shading closed across the three studied regions during heatwave periods, when the risk of overheating is highest, for both observed and simulated datasets.

<figure><img src="image_3.png" /><figcaption>Figure 2: Comparison of the mean daily profile of the simulated behavior of the French building energy code) and the observed behavior during an experimental campaign with windows and solar shading according to the three French regions considered (GIR: Gironde-Southwest; IDF: Ile-de-France – Paris; SE: South Est)</figcaption></figure>

3.2.1. *Mean daily profile - Window operation.* Behaviors from the regulatory simulation exhibit a clear pattern: in the GIR and SE regions, windows remain consistently open at night and almost entirely closed during the day. While real-world observations follow the same general trend (more windows open at night than during the day), not all occupants follow this daily behavior, leading to a more averaged daily profile with smaller variation in the proportion of windows open between day and night compared to the behaviors in the simulation. This highlights the diversity in occupant behavior that is not captured in the regulatory model.

3.2.2. *Mean daily profile – Solar shading operation.* The French building energy code assumes that solar shading remains open at night to facilitate nighttime ventilation. However, this is in line with survey data that 70% of occupants open or partially open their solar shading during the night. During the day in Gironde (GIR – southwest of France) region, solar shading is frequently closed throughout the day, whereas in IDF, they remain less closed in both the observed and simulated cases.
3.2.3. *Mean daily profile – key findings.* Overall, the regulatory model captures broad behavioral trends but significantly underrepresents occupant diversity. This leads to a more homogeneous representation of window and shading use across dwellings. It tends to favor thermally efficient behaviors: to cool naturally by opening the window openings during the night and close the solar shading during the day. Additionally, spatial differences have minimal influence on observed behavior, with only slight variations in shutter use based on window orientation.

3.3. *Comfort gap*

3.3.1. *Comfort gap category description.* This section aims to describe the comfort gap between observed and simulated buildings with the French building energy code hypothesis. Figure 3 compares the degree-hours (DH) obtained from simulations with those observed. The dwellings are then categorized into three main groups based on the French building energy code's comfort thresholds. Buildings with DH below 350 are considered "comfortable", those between 350 and 1250 DH meet construction requirements but are not considered comfortable in summer, and buildings exceeding 1250 DH cannot be constructed without a mechanical cooling system. To illustrate the comfort gap, results are aggregated into three categories based this regulatory threshold (right graph). The "No significant comfort gap" category (grey) indicates that the simulated and observed DH values place the dwelling within the same comfort threshold. The two other categories highlight discrepancies: "Observed dwelling more comfortable" (blue) signifies that the observed DH is significantly lower than the simulated one, while "Observed dwelling less comfortable" (red) indicates that the observed DH is significantly higher than the simulated value.

<figure><img src="image_4.png" /><figcaption>Figure 3: Comfort gap analysis in regard of the French building code</figcaption></figure>

3.3.2. *Key elements.* Most of the dwellings (32 over 58) remain within the same category in both observed and simulated datasets. However, around 16 dwellings are less comfortable in reality than predicted by regulatory simulations, highlighting a critical negative comfort gap. In contrast, cases where observed dwellings are more comfortable than simulated ones are less frequent, affecting fewer than 10 dwellings and presenting a less severe issue. Based on the observations made in the previous sections, it seems logical that there would be a considerable number of dwellings less comfortable in reality, as in simulation occupants are considered to have more thermally virtuous behaviors that reduce overheating compared to those observed in the simulations. It is, of course, not possible to attribute this comfort gap solely to behavior hypothesis, as there are others simulation assumptions that also partly explain this gap. Further investigation is needed to explore the relationship between the comfort gap and the behavior observed in the dwellings. However, these differences in the comfort gap and the modeling of occupant behavior raise questions about the regulatory framework and its implications for building design and building comfort.

# 4. Discussion on occupant window and solar shading operation model in the French building energy code

In this section, we discuss and expand on elements from the international building code review [2], focusing our discussion on summer thermal comfort in French residential buildings during the summer period. Our results indicate that the occupant behavior model used in the French regulatory framework
assumes significantly more thermally virtuous window and solar shading behavior during summer than
what is observed in the experimental campaign. This discrepancy partly explains the comfort gap
between monitored and simulated dwellings. In order to reduce the comfort gap, the authors propose
two main avenues for improvement (1) in-use occupant behavior and a (2) better integration of occupant
behavior in the regulatory framework.

(1) Firstly, there is a necessity of promoting and supporting occupants in adopting thermally efficient behaviors to optimize indoor comfort, whenever possible. Secondly, there is a necessity to improve our understanding of the constraints that hinder the adoption of such behaviors (e.g., risk of intrusion; insect issues...). Occupant behaviors would therefore align a bit more closely with those considered in the regulations, leading to a reduction in the comfort gap. However, it is highly unlikely that all behaviors will be the same as those in the regulation, which is why, according to the authors, it also seems necessary (2) to adjust the window and solar shading operation model implemented in the regulation, as it appears too optimistic. Such an adjustment would help ensure that buildings remain comfortable even when occupants adopt less thermally efficient behaviors. One possible approach is to modify certain coefficients, such as the window opening ratio, to create a more conservative model. Another, more refined, approach would be to account for behavioral diversity by running multiple simulations with different occupant behavior scenarios or models. In this case, buildings would need to meet a predefined comfort threshold under each scenario to allow the construction. However, this approach presents challenges, particularly by increasing the computational cost of regulatory simulations and making the results more complex for engineering firms to interpret. Both solutions would impose stricter comfort criteria, potentially making it impossible for certain dwellings—such as top-floor apartments—to meet comfort thresholds. In such cases, mechanical cooling might become necessary for these buildings to be considered comfortable. This raises the need for a trade-off assessment between health-related benefits and the increased energy consumption associated with air conditioning.

**5. Conclusion**

This study compares window and solar shading operation behaviors and their associated discomfort levels, using the DH indicator, during the summer. The study is based on an experimental campaign of 58 dwellings across three French regions, and on simulations of the same buildings using the Building Performance Simulation Software Cometh RE2020 of the French building energy code. The findings quantify the extent to which occupant behavior as modeled in regulatory framework can deviate from observed behaviors in an experimental campaign and its impact on comfort gap.

While the regulatory simulation captures general behavioral trends based on time of day and indoor and outdoor temperatures, it significantly underrepresents behavioral diversity. Unlike real-world observations, the regulatory model exhibits no variation between different rooms of similar dwelling, and minimal variation between different dwellings. These discrepancies partly lead to a significant comfort gap between simulations and actual occupant comfort in 2023 summer, with 16 out of the 58 (28%) dwellings proving to be significantly less comfortable than predicted.

To reduce the comfort gap while ensuring that buildings remain thermally comfortable during the in-
use phase, two main approaches can be considered: (1) Supporting occupants in adopting thermally
efficient behaviors by understanding and addressing the barriers to their implementation. (2) Improving
the realism of occupant behavior models in French building energy code, for instance, by incorporating
a moderation coefficient to adjust the window opening ratio or to incorporate multiple scenarios/models
of behavior. Further research is needed to refine these approaches and assess their potential impacts on
occupant comfort, health and the energy consumption within different buildings context and prospective
weather.

**Acknowledgements**

This work was done within the framework of Solar Academy Graduate School, grant ANR-18-EURE-0016. The authors would like to acknowledge the financial support of CSTB, as well as the contributions of those involved in the RENOPTIM database and the occupants of the instrumented dwellings.
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