Smart Matter-Enabled Air Vents for Trombe Wall Automation and Control

Abstract

Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings.

1. Introduction

The climatic emergency and the requirement for sustainable energy management have spurred the increasing use of energy-efficient approaches within the building industry [1]. Buildings in the European Union are responsible for almost 40% of all energy consumption and for 36% of CO₂ emissions and therefore are a prime target for policies for decarbonization [2]. To this end, the European Directive 2018/844/EU (EPBD) [3,4] established the Nearly Zero-Energy Buildings (NZEB) principle, obliging new buildings by design to achieve nearly zero energy consumption at the net level [5,6,7].

Passive solar systems offer a promising solution for enhanced thermal comfort and reduced energy use since they exploit natural energy transferring processes autonomously without supplementary mechanical HVAC [8,9]. Of the many passive solar systems, the Trombe wall is prominent for its capacity for accumulating solar radiation during the day and discharging it gradually inside for improved comfort and lower heating loads. Originally developed by Félix Trombe and Jacques Michel during the 1950s, the system is composed of a massive wall, an air gap, and an outer glazing surface, producing a greenhouse effect and allowing heat storage and its gradual release to the interior of the building [10].

Its working, nonetheless, is highly sensitive to design parameters like wall thickness as well as to material characteristics, shading conditions, and the behavior of air vents [11,12]. Historically, since the working of vents has involved intermediate human intervention for control, this has constrained efficiency and has precluded acceptance in modern intelligent buildings where autonomous responses play a vital role.

There have also been recent investigations into the automation of Trombe walls by sensor–actuator networks. For example, Briga-Sá et al. [13] illustrated that automatic control by solar radiation (RS), cavity temperature ($T_{\mathrm{cam}}$), and indoor temperature ($T_i$) optimizes heat storage cycles for increased comfort and energy efficiency. Extending this trend further, the incorporation of the Internet of Things (IoT) technologies has made it possible for real-time monitoring and automatic control in passive systems  [14,15].

There have also been some proposed communication protocols for smart building automation, such as Zigbee, Thread, MQTT, and LoRa  [16,17,18]. Though each has particular strengths in range, cost, and energy efficiency, many need dedicated gateways and have interoperability issues. Wi-Fi, for all its higher power consumption, has the best deployment density in current infrastructures. More recently, there has also come into existence the Matter protocol  [19,20], a standardized, secure, and interoperable smart device platform that has a strong ecosystem and intrinsic smart home platform compatibility.

This work is aimed at conceptualizing and developing an automatic control system through IoT that optimizes Trombe wall performance. In this system, low-cost vent actuators and temperature sensors, controlled by the Matters protocol, are applied for automatic control of airflow according to real-time conditions inside and outside the cavity.

By embracing the use of passive solar design and interoperable IoT solutions, this work has the potential of furthering NZEB objectives by fostering cost-effective and scalable solutions for automation.

This article is divided into five sections, besides the Introduction section. Section 2 highlights the research work developed in the field, focusing on the development of Trombe walls, regarding the influence of ventilation and shading device management, and the need to introduce IoT as a way to enhance the operation of buildings without users intervention, focusing on the work developed by Briga-Sá et al. [13]. Section 3 of this work presents the methodology. The IoT system defined for this work starts with the hardware solution, made up of the actuator and sensor microcontrollers. The firmware solution is defined by an operating system for central control of the entire system, dealing with the data sent by the sensors and sending commands to the actuators as desired. The microcontroller control firmware acts as an interface between the automation system, the user, and the system’s own actuators/sensors. Section 4 presents the discussion of results and the practical implications of this work in an automated smart home system. Finally, Section 5 highlights the conclusions, the main findings are presented, and future works to improve the system are suggested.

2. State of the Art: Trombe Walls and the Integration of IoT Technologies

Trombe wall is a passive solar system that has been proven highly effective in building energy efficiency. It operates based on the principles of heat transfer. The incident solar radiation in the glazing is stored in the air gap due to the greenhouse effect and gradually released through the massive wall by conduction, radiation, and convection. When the air vents are open, the heat transfer is improved by air convection, maximizing heat gains to the interior of the building. The optimization of heat transfer processes can lead to a decrease in the requirement of active heating systems. This solution is particularly suitable in climate zones with high solar radiation intensity and large daily temperature fluctuations [13]. Previous research also highlights the impact of ventilation in the Trombe wall thermal performance depending on the heating and cooling seasons [21]. In this context, VTW and NVTW systems should be adapted to the climate zone conditions and building use requirements.

The thermal performance of the Trombe wall is the result of a multifaceted interaction of factors, which includes the thermal inertia of the Trombe wall materials, the glazing type, and the shading devices [22,23,24]. The introduction of this type of solution can cause a decrease of up to 16.36% in the heating demand for residential buildings [25].

An updated review developed by Elsaid et al. [26] examined several configurations and optimization strategies applied to Trombe walls as heat and/or cool wall technology. The study showed that while the traditional Trombe wall can provide up to a 30% reduction in buildings energy consumption, there is great potential for enhancement by modifying the classic model. The most effective strategies incorporate phase change materials (PCM) [27,28,29], yielding energy savings in winter of up to 55% and 36% in summer, and include other elements, such as photovoltaic cell (PV) curtains and thermal insulation layers [30]. Furthermore, it is also highlighted that the management of the ventilation system is crucial to improve heat gains and thus indoor comfort. In this context, a Trombe wall can be defined as ventilated if vents are included in the system.

With this, the main difference between the VTW and the NVTW relies on the improvement of heat transfer by air convection through the air vents. NVTW is mainly based on conduction through the massive wall, and its thermal efficiency is highly dependent on the properties of the wall, while the VTW has openings in the massive wall, allowing air circulation, which increases heat transfer by convection [31,32].

Experimental and numerical studies prove that the addition of ventilation can enhance system efficiency. In the heating season, the VTW exhibits better thermal gains as a result of the forced convection effect, whereas the NVTW is more suitable for periods of gradual and steady heat transfer [33].

Empirical research shows that for a Trombe wall with solid brick, thermal gains are more evident when there is no ventilation. On the other hand, when ventilation openings are introduced in the massive wall the convection process is intensified, leading to quicker heating of indoor spaces. Quantitative studies demonstrate that the addition of ventilation within a granite wall produced 44.06% higher thermal gains in comparison with the NVTW case [31].

Experimental and computational tests carried out in Portugal, for a test cell with a Trombe wall subjected to real climate conditions, showed appreciable thermal responses for both configurations. The NVTW presented high thermal inertia, which was expressed by smaller indoor temperature variations and higher capacity for nighttime heat retention. The VTW presented better thermal performance during daytime, allowing indoor spaces to be heated faster, but this came with more nighttime heat loss if the air vents are correctly opened [25].

Further studies validate that the combination of controlled ventilation with shading devices greatly enhances the functional effectiveness of Trombe walls [14,34]. Computer simulation reveals that the use of external shutters can minimize solar heat gain by as much as 80% during summer, and thereby lower the risk of overheating [35].

Results for both types of Trombe wall revealed that the thermal performance can be attained through a hybrid system combining the potentialities of VTW and NVTW. The operation of the VTW configuration in the daytime and NVTW configuration at nighttime has been shown to have the potential to achieve optimum overall system thermal performance [35]. This mixed operating mode allows optimization of the heat transfer processes by two mechanisms: taking advantage of the daytime accelerated heating effect from the ventilation system of the VTW and, during the night, changing to the NVTW mode by closing the ventilation gaps, reducing thermal losses. The inclusion of automated systems for controlling ventilation and for shading device operation makes it possible to manage the thermal gains precisely according to environmental daily changes and indoor air comfort requirements. Experimental results validate the efficacy of this strategy, proving that the use of an automated control system to modulate ventilation openings leads to an appreciable enhancement in the energy behavior of the Trombe wall, while at the same time avoiding manual user intervention [36].

The use of Internet of Things (IoT) technologies in the control of Trombe walls is an important step forward in the optimization of energy efficiency in buildings that requires more investigation. In comparison to classical systems, where ventilation openings and shading devices need to be manually adjusted, the use of automated systems, relying on sensor networks, microcontrollers, and wireless communications protocols, allows dynamic control of such parameters. The indoor temperature ($T_i$), outdoor temperature (Te), and solar radiation (SR) continuous monitoring by means of integrating sensor systems enables the automatic and adaptive response to climatic conditions and indoor requirements. This approach significantly enhances the system’s thermal efficiency, without the need for human intervention. In addition, the application of machine learning algorithms enables the prediction of thermal patterns and the most favorable heat storage and release cycles, giving the system a greater adaptive capacity to seasonal variations [37]. This would allow good optimization of the heat transfer, contributing to a considerable saving in energy consumption for heating and for cooling, when cross ventilation is guaranteed [38].

However, these types of technologies are still rare regarding their application in passive solar design by indirect gains, as in the case of Trombe walls, showing that bioclimatic design and smart buildings can be integrated to achieve more sustainable and eficient buildings.

The integration of Trombe walls in smart homes remains an unexploited field of research in the scientific literature, despite the recent advances in automation and energy efficiency. Despite some research demonstrating that ventilation, material, and shading devices directly influence the thermal behavior of the wall, most research focuses on pre-programmed control strategies, without incorporating adaptive strategies or machine learning for dynamic system optimization [14].

Heat storage and release efficiency of the Trombe wall can be optimized by adding advanced sensors and high-performance microcontrollers, which can provide real-time monitoring and more precise thermal flow control. The utilization of solar radiation, humidity, and temperature sensors has been established in research studies; however, they are limited in deployment because they lack integration with advanced systems and contemporary communication standards like Matter, Thread, and LoRaWAN, which provide high reliability and low energy consumption. Recent studies have demonstrated that the use of LoRaWAN networks allows for effective remote management of thermal systems, making direct user intervention unnecessary and enhancing the responsiveness of the system to climatic fluctuations [15].

Matter is a newly created application-layer protocol that was launched by the Connectivity Standards Alliance (CSA), with major industries such as Apple, Google, and Amazon backing it. Its purpose is to address fragmentation of the smart home market [20] by offering native interop between platforms such as SmartThings, Alexa, Google Home, and Apple HomeKit. In contrast to typical solutions such as Z-Wave or Zigbee, Matter runs over standard IP networks, most often with Thread and Wi-Fi, and hence is compatible with installed bases while still being capable of offering low-power mesh networking capabilities [39]. Its security is rooted in certificate-based authentication and robust cryptography, hence lowering vulnerabilities common for lighter-weight protocols such as MQTT.

While still in its initial stage of implementation and with some problems related to device categories and latency still unresolved, Matter has enormous industrial support and open-source deployments to become a potentially enduring standard. Moreover, its platform support, like Home Assistant, provides for smooth commissioning and control without proprietary gateways, thereby complementing its role in building automation applications like intelligent vent control in Trombe walls.

Aside from communication, the automation and decision-making optimization represent a significant challenge. Predictive models that utilize machine learning can potentially enable the Trombe wall to dynamically adjust ventilation openings and shading device operation to improve thermal efficiency. It has been researched that adaptive algorithms can predict climatic changes and alter the system accordingly based on the energy needs of the surroundings, which further reduces the need for active heating and cooling [13].

Current research highlights that, even with the development of computational modeling, empirical validation of Trombe walls integrated with IoT systems is scarce. A significant area of interest is the use of a decentralized and autonomous control system to enhance real-time efficiency [40,41]. A few investigations [42,43] have considered the application of cloud computing-based remote control and predictive models, allowing greater responsiveness to seasonality and making the technology feasible for application in mixed-use buildings. In this context, the implementation of IoT systems requires an evaluation of the available wireless communication technologies. The Wi-Fi protocol, which is typified by high data transfer rates, has high energy consumption and is thus more appropriate for use in residential and commercial buildings. The Thread protocol, on the other hand, provides an energy-efficient alternative and mesh network support and is thus highly appropriate for IoT systems that comprise several interconnected devices [44]. For applications requiring long-distance communication, the LoRaWAN protocol emerges as the preferred solution, combining low energy consumption with extensive range [45]. Empirical proof of the validity of this technological solution is provided by practical applications, especially in agro-industrial settings, where thermal control is important both for thermal comfort and energy efficiency. A case study showed that the incorporation of sensors in a LoRaWAN network, with automatic actuation over ventilation openings, led to a great decrease in indoor thermal amplitude and a large drop in energy expenditure associated with climate control [15].

Following the identified research gap, the current research intends to introduce innovation through the development of an enhanced and automated control system based on high-precision sensors, effective communication protocols, and intelligent algorithms for optimizing Trombe wall efficiency in contemporary architecture.

3. Methodology

The methodology followed in this research sought to illustrate the viability of integrating an upgraded control system in a classic Trombe wall and prioritizing the use of cost-effective solutions that are Matter protocol-compatible. The system was conceived, developed, and evaluated in an experimental cell under real climate conditions.

This infrastructure was designed to link sensors, actuators, and control devices through the Matter protocol, an application protocol developed by a consortium of big tech companies as referred in Section 2 over Wi-Fi 802.11. The control interface hosted a top Home Assistant OS on a Raspberry Pi 5, by Raspberry Pi Foundation, which served as the hub automating point.

When selecting the components to be used in the system, high performance and compatibility were used as guidelines, taking into account high reliability, low energy use, and compliance with existing communication protocols [39,45]. Effective communication between the sensor, actuator, and controller in the system is facilitated via wireless microcontrollers [15]. A specially programmed firmware encompasses all the elements so that the system is able to respond quickly and precisely to changes in environmental conditions. This configuration allows for self-adjusted modifications to ventilation settings, shading device positions, and air flow rates, thus increasing the thermal efficiency of the Trombe wall.

Automation is a critical component of this system, allowing it to operate autonomously, constantly adjusting to changing environmental conditions and the building’s thermal needs. This flexibility not only improves occupants’ thermal comfort but also optimizes energy efficiency by reducing the use of traditional HVAC systems.

3.1. Characterization of the Trombe Wall Experimental Device

The Trombe wall followed a conventional building design with a brick wall having a thickness of 34 cm and plaster on both sides. A 6 cm air gap was provided between this wall and the outside glazing to allow natural convection. The outside glazing consisted of an uncolored double-glazed panel, whereas the outer face of the massive wall was painted black in order to maximize the absorption of solar radiation.

The methodology is based on previous work carried out in the same test cell located on the Universidade de Trás-os-Montes e Alto Douro (UTAD) Campus and subject to real weather conditions [13,15], ensuring comparability between data and scientific continuity in the research group, and whose characteristics are indicated below. This experimental device supported further studies to analyze the possibility of implementing automation and control of its operation. A test cell was built, consisting of a thermally insulated metal container, 6 m in length, 2.4 m in width, and 2.3 m in height, yielding an internal usable volume of approximately 33 m³ and an area of 14 m². The southern facade of the test cell integrated a Trombe wall to maximize solar incidence and minimize unwanted shading effects.

In the interest of reducing the thermal losses, extruded polystyrene (XPS) thermal insulation was applied to the interior surface of the floor, roof, and side walls, in line with the thermal performance requirements needed for building appraisals.

Ventilation was provided by including four upper and four lower air vents, which were approximately 2% of the total surface area of the Trombe wall. These openings allowed the consideration of two primary operational configurations: the non-ventilated Trombe wall (NVTW) and the ventilated Trombe wall (VTV), thus allowing for unique processes of heat transfer by conduction and convection.

The characteristics of the experimental device of the Trombe wall that supported this research work, with regard to the construtive details and the location of the different sensors, are shown in Figure 1.

3.2. Trombe Wall Performance Under Real Climate Conditions

Three scenarios for heating and cooling performance were evaluated. In this study, the focus is on the results obtained from the second scenario, based on an appraisal of which was the most suitable operation algorithm depending on the influence variables, such as the authors concluding that the second scenario was the best scenario from a thermal control perspective. Therefore, the other scenarios are not referred to. This test scenario produced the most effective thermal stabilization, minimizing heat loss during the night and optimizing heat gains during the day. This scenario is based on independent control of both the blinds and the interior vents. The shutter was controlled according to the solar radiation (RS), opening when RS > 100 W/m² and closing when RS < 50 W/m², while the openings were regulated by the thermal differential between the air cavity and the interior space ($T_{\mathrm{cam}}-T_i$ > 15 °C to open; < 10 °C to close).

The graphical representation of the thermal variations observed during this selected period is shown in Figure 2. The control strategy was modified, separating the operation of the ventilation openings from the regulation of the shading device. While the blind remained controlled by solar radiation, using the same thresholds as in the first test, the ventilation was now regulated based on the thermal differential between the air cavity and the indoor space ($T_{\mathrm{cam}}-T_i$). This approach ensured that heat transfer occurred only when the thermal differential indicated a benefit for the indoor environment.

The results demonstrated that this configuration provided better thermal balance, significantly reducing nighttime heat losses while allowing the massive wall to retain and release heat more efficiently throughout the daily cycle. A comparison between this scenario and the first revealed that, while the initial configuration caused abrupt indoor temperature variations in response to external fluctuations, the strategy adopted in the second test scenario ensured more effective thermal stabilization, leading to a more optimized system performance in achieving the required indoor air temperature.

3.3. Development of the Automation and Control System

The system comprises a Matter-compatible smart ventilation grid with temperature and humidity sensors, as well as the necessary hub to commission and control the devices to a smart home network. In the following subsections, the methodology adopted to develop an automation system to control and optimize the performance of the Trombe wall will be described.

3.3.1. System Architecture

Here, a single automated vent was employed and tested for proof of concept. The main idea was to prove the possibility of integrating 3D-printed ventilation grids, affordable sensors, and smart microcontrollers under a Matter-enabled smart home environment. Even though the testing was only for a single vent, it was a highly modular and scalable system that can easily be replicated for many vents or combined with other building control units. This therefore acts as a precursor for the wider usage of the Matter-based home automation system, giving technical credibility for mass-scale installations in the future.

The system architecture is composed of interconnected hardware and software components designed to enable smart ventilation control. As illustrated in Figure 3, the setup includes an ESP32-C6-DevKit-C1, developed by Espressif Systems, running Tasmota firmware configured as a Matter node. This device interfaces with two sensors to monitor temperature and humidity—the DHT11 to monitor the air gap and the SHT41 to monitor the indoor environment. Furthermore, the microcontroller also controls an SG90 servo motor that operates a 3D-printed ventilation grille. A Raspberry Pi 5 running Home Assistant, along with the Matter Server add-on, acts as the control hub, managing communication and automation within the smart environment.

3.3.2. Development of the 3D-Printed Vent Grid

The starting point for the geometry of the system was the open-source model “Yet Another Smart Vent” [46], which was significantly adapted using CAD software to match the 22 × 15 cm openings of the Trombe wall under study. In addition to dimensional adjustments, the design was modified to incorporate the SG90 servo motor and the ESP32-C6-DevKit-C1 microcontroller laterally, positioned beside the airflow path and mechanically decoupled from the air vents. This design approach ensured both functional protection of the electronics and optimized internal ventilation flow.

To physically implement the smart ventilation system proposed in this work, as seen in Figure 4, a custom-designed vent grid was developed using additive manufacturing. The objective was to create a compact, modular component capable of integrating a servo actuator and sensing module, while maintaining efficient airflow through the Trombe wall air vents.

The grid was printed in ABS (acrylonitrile butadiene styrene), selected for its mechanical strength and temperature resistance, which is crucial for prolonged operation under sunlight exposure and temperature fluctuations. Printing was performed using a Bambu Lab X1-Carbon, produced by Bambu Lab, with a 0.4 mm nozzle and standard layer height of 1 mm.

Assembly involved manual fitting, sanding of interlocking joints, and gluing where necessary. The final component was inserted into the Trombe wall aperture using a tight friction fit and was sealed around the edges using neutral-cure silicone to ensure airtightness without permanent fixation.

The electronics are currently exposed in the prototype stage, a design choice that facilitates testing and debugging. In future iterations, these will be enclosed in dedicated protective compartments integrated into the printed structure.

A single prototype has been printed and assembled. It is currently operating on a test bench, responding to temperature thresholds defined in the Home Assistant logic described earlier. Full installation into the Trombe wall is scheduled for the next phase of the experimental campaign. A photograph of the grid in its installed location will be included in Section 4.

3.3.3. Hardware Solution

The proposed system is predicated on the use of a small, cost-effective hardware base for thermal control in passive solar uses, integrated in the 3D smart vent. The basic components are included in

Table 1. System components, specifications, and typical power consumption.

Component	Type	Qty.	Range	Acc.	Comm.	Notes	Power Cons.
DHT11	Sensor	Temp.	0–50 °C	±2 °C	1-wire	1 °C res.	∼0.5–1 mW (active)
		RH	20–90 %	±5 %	1-wire	—	—
SHT41	Sensor	Temp.	−40–125 °C	±0.1 °C	I2C	0.01 °C res.	∼0.4 mW (active)
		RH	0–100 %	±1.8 %	I2C	0.01 % res.	—
SG90 Servo	Actuator	Angle	0–180 °	±1 °	PWM	∼1.8 kg·cm; 0.12 s/60°	Idle ∼50 mW; Peak 2.5–3 W
ESP32-C6	Controller	—	—	—	Wi-Fi, Thread	32-bit MCU	Idle ∼200 mW; Wi-Fi TX up to 500 mW
Raspberry Pi 5	Controller	—	—	—	Wi-Fi, GPIO	Home Assistant	5–7 W (continuous)

. The ESP32-C6 microcontroller is responsible for executing the control logic and communicating over the Matter protocol. To actuate the vent opening mechanism, the microcontroller controls the SG90 servo motor via PWM from GPIO10. To monitor the air cavity temperature inside the Trombe wall ($T_{\mathrm{cam}}$), DHT11 is connected via a single-wire digital interface to GPIO07, and the indoor air temperature ($T_i$) is measured using SHT41 via I²C interface (SDA on GPIO02, SCL on GPIO11). The automated vent with the sensors is powered using a standard 5V USB type-C power supply, providing enough current to the entire system. Furthermore, a Raspberry Pi 5 is used to run Home Assistant to commission and control the developed device.

The physical connections between the actuator, the sensor, and the microcontroller are illustrated in Figure 5.

The temperature measurement system relied on two sensors of different functionalities and communication interfaces. A DHT11 was utilized for air cavity temperature ($T_{\mathrm{cam}}$) sensing, acting through a single-wire digital communication interface. Even though this sensor has a secondary output concerning humidity levels, temperatures were only taken into account for control. It was selected for its commercial off-the-shelf quality, along with the fact that it was readily available and natively supported by Tasmota-activated modules, thus enabling fast prototyping and minimal integration effort. The indoor ambient temperature ($T_i$), as a reference for implementing ventilatory action through triggering, was measured instead by an SHT41 connected by means of an I2C communication interface. It was selected for its higher quality and long-term reliability, aiming for the stable and reliable thermal feedback needed for the control algorithm. This system not only guaranteed correct interior condition monitoring but also demonstrated that the system proposed can operate under heterogeneous sensors and diversified communication protocols (I2C and single-wire). The SG90 servo motor is powered and controlled by the ESP32-C6 directly through PWM on GPIO10 and allows for the precise movement of the position of the air vent for efficient heat transfer between the indoor space and the air cavity through precise adjustment of the motor’s movement.

The ESP32-C6 was chosen because of its native support for the Matter protocol, its dual-band Wi-Fi feature, and its power efficiency. The component selection criteria prioritized aspects like availability, modularity, and current limits suitable for standard USB-powered devices. The overall hardware system operates well within the specified USB current profiles, even when involved in active actuation stages.

The final design criteria prioritized the following:

• Affordability: using off-the-shelf components;
• Modularity: allowing replication and integration in similar control systems;
• Low-power energy: supporting continuous operation from basic 5V power sources.

3.3.4. Firmware Solution

The firmware component plays a central role in the operationalization of the Trombe wall automation system, enabling integration between sensors and actuators of various types and the central control platform itself. The system device layer is responsible for reading and sending sensed parameters, executing control commands (opening or closing the vents), and communicating bidirectionally with the Home Assistant via the Wi-Fi communication protocol and the Matter application protocol. The Home Assistant platform is responsible for receiving the sensor data, executing the defined control algorithm, and sending the commands to the device. The proposed architecture adopts a modular approach based on open-source firmware and interoperable protocols, guaranteeing the flexibility needed for future expansion of the system. The ESP32-C6-DevKit-C1 microcontroller is programmed with the Tasmota32 firmware, which offers support for controlling servo motors and reading environmental sensors.

At the same time, the sensors are integrated directly in the Tasmota ecosystem, ensuring redundancy and reliability in thermal monitoring. This configuration makes it possible to implement local control logic based on the thermal differential between the air chamber and the interior of the building, which translates into an automated, efficient ventilation system adapted to the thermal comfort requirements in real time.

The following sections describe in detail the configuration of the firmware in order to control the actuators according to the data received by the sensors, the parameters used, and the process of commissioning the device on the Matter network, as well as the automations defined in Home Assistant to ensure automatic and intelligent management of the vents.

With this, there are three main components that make up the structural build of the Trombe wall IoT system: the Tasmota32 firmware running on the ESP32-C6, the Home Assistant OS installed on a Raspberry Pi 5, and the Matter protocol stack. Firmware installation is carried out using the ESP Web Tools via the official Tasmota repository [47], which enables native control of both the SG90 servo motor and the DHT11 temperature/humidity sensor. Within the Tasmota configuration, the shutter mode is activated using specific command sequences that enable PWM output for vent control and require the definition of both opening and closing durations. All the commands are presented in Listing 1.

In addition to the DHT11, the system also integrates an SHT41 sensor to provide high-precision indoor air temperature measurement ($T_i$). This sensor is managed through Tasmota and instead communicates with the ESP32-C6 via the I²C interface. The presence of both sensors ensures independent and stable readings for the air cavity temperature ($T_{\mathrm{cam}}$) and the indoor temperature ($T_i$), thereby enhancing the system’s ventilation control logic by enabling more accurate and timely actuation. These two commercially available sensors were selected for integration due to their accessibility and affordability. Despite having different communication protocols, both demonstrate similar responses to temperature variations, therefore providing consistent environmental data collection. As an off-the-shelf solution, this choice validates how distinct communication standards can co-exist within a unified system architecture, providing flexibility in sensor deployment.

Listing 1. Tasmota configuration for servo motor.

The firmware controls all the infrastructure that is interfaced with the ESP32-C6-DevKit-C1 board. Used in home automation on a large scale, the firmware using Matter functionalities allows seamless integration of devices regardless of proprietary platforms. It is used here for the control of the SG-90 servo motor, by GPIO10, which regulates the opening of the ventilation grille of the Trombe wall for optimizing the system’s energy performance. Also, the DHT11 sensor is configured in Tasmota as a module, assigned to GPIO07 and labeled as “DHT11”, enabling continuous monitoring of the air cavity temperature ($T_{\mathrm{cam}}$), and in parallel, the SHT41 sensor is integrated into the system via the I²C interface, with GPIO02 designated for the SDA line and GPIO11 for the SCL line, facilitating round-the-clock temperature condition monitoring of the system, as seen in Figure 6.

The control algorithm implemented in this work was intentionally simple, based on the temperature differential $\Delta T=T_{\mathrm{cam}}-T_i$, where the vent opens if $\Delta T>15$ °C and closes if $\Delta T<10$ °C. This choice follows previous research by Briga-Sá et al.  [13], in which the cavity air temperature ($T_{\mathrm{cam}}$) was shown to act as a direct proxy for solar radiation influence, due to the greenhouse effect inside the Trombe wall.

3.3.5. Commissioning the Device Using Home Assistant and Automation Control

Home Assistant operating system installation was made easy with the use of Raspberry Pi Imager, present at the official Raspberry Pi site [48]. Matter integration is achieved by adding the Matter Server add-on to Home Assistant, where the commissioning will be performed. The ESP32-C6 acted as a Matter Node and worked with Home Assistant through the Matter Server. This was achieved via a manual pairing code on the commissioning process, copied to Matter Server add-on to add the device. Tasmota’s Matter settings had to enable the commissioning mode for the Tasmota device, as presented in Figure 7. By this, the device became a member of the Fabric, which is the Matter device group, so that it can be recognized as a type of actuator “shutter”, which has a specific purpose of controlling the air vents of the Trombe wall.

Control protocols in automated form were incorporated in the Home Assistant 16.0 software to control the shutter based on the temperature gradient detected between the air chamber of the Trombe wall and the indoor environment. The shutter is automated to open when the air chamber temperature exceeds the indoor temperature by more than 15 °C, while the shutter is automated to close when this temperature difference drops below 10 °C. These specified thresholds allow for passive thermal control based on the working principles of the Trombe wall.

Rather than using the graphical interface, the automation logic was inserted by hand into the automations.yaml file using the File Editor add-on in the Home Assistant. The system evaluates the conditions listed every 10 s and, if the conditions are a match, carries out the corresponding action. Listings 2 and 3 show the two YAML blocks containing this logic.

Listing 2. Automation to open the shutter when Tcam − Ti > 15 °C.

Listing 3. Automation to close the shutter when Tcam − Ti < 10 °C.

One of the standout features of this automation system is the use of the virtual switch input_boolean.manual_override, as seen in Listing 4. This is a device that serves as an override signal whose activation to “on” temporarily overrides all automatic control of the shutters, thus allowing for manual user input without interference. This essentially stops unwanted cycling or conflicting commands and ensures that the system enjoys a stable and user-friendly experience during manual use or maintenance.

Listing 4. Automation for temporary lockout after manual shutter control in the automations.yaml file.

The File Editor add-on was also utilized in Home Assistant configuration file editing, in which certain entries were made in the configuration.yaml file (Listing 5):

Listing 5. Added lines to configuration.yaml file.

Secure remote access to Home Assistant was made possible with the integration of the HA add-on DuckDNS, and in order to add another level of communication security we used the HA add-on NGINX SSL Proxy, providing end-to-end encryption and stable communication using the HTTPS protocol. The configurations allow Home Assistant to identify and accept secure connections from the NGINX proxy server while using the proper header ‘use_x_forwarded_for’ to get the original client public IP address. Subsequently, an authentication attempt limit has been added with a cap of three consecutive failed logins before the user’s IP address is blocked. This helps mitigate unauthorized access, as well as brute-force attacks.

This modular and extensible scheme enables the incorporation of new devices into the Matter network without changes to the current infrastructure. The choice of Tasmota as the primary firmware, combined with Home Assistant as the automation platform and Matter as the communication protocol, ensures that the system remains compatible with future expansions, maintaining robustness and efficiency in the automated management of the Trombe wall ventilation system.

4. Results and Discussion

This section presents the evaluation of the developed smart vent prototype for Trombe wall systems. The analysis begins by validating the mechanical design and installation of the 3D-printed vent, ensuring proper actuation with the designed hardware and firmware. Furthermore, the device is successfully commissioned into a Matter network, enabling real-time access to sensor data and actuator control through Home Assistant. Next, in this smart home platform, the implementation of the automation logic based on the literature is presented, followed by a discussion of integration challenges, deployment limitations, and considerations for scalability and future improvements.

4.1. Evaluation of the 3D-Printed Air Vent Prototype

Through functional testing under laboratory conditions, the custom 3D-printed smart air vent developed for this project was assessed. The prototype was primarily evaluated on a bench setup to verify mechanical stability, actuation response, and compatibility with the embedded control logic defined in the Home Assistant platform.

The structural performance of the air vent was satisfactory. Printed as separate components due to build-plate limitations, the final assembly showed strong mechanical integrity. Adhesive bonding combined with friction-based assembly and silicone seals offers precise positioning and ensured no perceptible air leakage during the actuation cycles. The choice of using ABS instead of more common PLA (polylactic acid) provided thermal resistance for passive solar application, especially when subjected to the possibility of elevated temperatures along the Trombe wall surface.

The SG90 servo motor exhibited consistent and accurate motion from one extreme to the other, all within the specified PWM range. With five thinner modified blades, it encouraged smooth and quiet modulation of airflow into the wall cavity with no observable vibrations or mechanical backlash. The side mount for the ESP32-C6-DevKit-C1 with the servo motor proved advantageous for a compact modular structure while maintaining the full vertical airflow path for natural circulation.

Figure 8 shows the 3D-printed grid, during both bench testing (a) and in its installed state on the Trombe wall (b). The current installation for operation on a single vent, however, has successfully shown autonomous behavior: autoregulating open and close positions based on the preconfigured temperature thresholds ($T_{\mathrm{cam}}-T_i$ > 15 °C to open and $T_{\mathrm{cam}}-T_i$ < 10 °C to close) configured therein on the Home Assistant automation rules. This prototype is capturing data on the environment and is responding as designed, demonstrating the practical feasibility of the suggested smart vent control architecture.

4.2. Commissioning the Tasmota Device

The seamless integration of the ESP32-C6 device, running Tasmota 14.6.0 firmware, into the Matter ecosystem was achieved through Home Assistant’s Matter Server. The actuator was connected via Wi-Fi and configured to run on the Matter protocol, thereby providing natively bidirectional communication with the server for controlling the Trombe wall shutter system. This integration provides the system with the capability to open or close the ventilation grille adjustively according to the thermal differential measured by temperature sensors.

As is evident from Listing 6, during commissioning, the Matter Server logs show that there was a secure communication session established between Home Assistant’s Matter Server and Tasmota on the ESP32-C6 device. A mandatory resubscription operation was initiated, as shown by the message for opening a CASE session for the subscription entry. This led to a successful handshake for CASE, marked by the transition of the SecureSession state from kEstablishing to kActive. The Matter Server then sent a SubscribeRequest to the device, starting the subscription. The affirmative response acknowledged the server to have successfully completed the handshake process and was now in an active subscription with the ESP32-C6 device, in which the SubscriptionID was active and reporting intervals were set between 1 and 60 s. This secure subscription enables the server to receive sensor data and transmit actuator commands in real time with interoperability and network robustness.

Listing 6. Matter server console logs.

2025 -05 -30 16:37:56.064 CHIP_PROGRESS [ chip . native . DMG ] Trying to establish

a CASE session for subscription

2025 -05 -30 16:37:56.364 CHIP_PROGRESS [ chip . native .SC] SecureSession [...]

State change ’ kEstablishing ’ --> ’kActive ’

2025 -05 -30 16:37:56.366 CHIP_PROGRESS [ chip . native .EM] <<< Msg TX [...]

Type 0001:03 (IM: SubscribeRequest )

2025 -05 -30 16:37:59.015 CHIP_PROGRESS [ chip . native . DMG ] Subscription

established with SubscriptionID = 0 x0000ecf6 MinInterval = 1 s

MaxInterval = 60 s Peer = 01:0000000000000008

Moreover, the Tasmota logs demonstrated in Listing 7 confirm that the firmware was initialized properly and the Wi-Fi connection was established without any hitch, ensuring the actuator communicates efficiently with the Matter Server and act upon shutter opening and closing commands according to the thermal conditions being monitored.

Listing 7. Tasmota console logs.

00:00:00.403 MTR : Loaded 1 fabric (s)

00:00:02.829 MTR : Configuring endpoints

00:00:02.842 MTR : endpoint =    2 type : shutter shutter :0

00:00:02.845 MTR : endpoint =    3 type : temperature filter : SHT4X #

Temperature name : sht

00:00:02.848 MTR : endpoint =    5 type : temperature filter : DHT11 #

Temperature name : dht

00:00:02.859 RSL : RESULT = {‘‘ Matter ’’:{‘‘ Initialized ’’:1}}

00:00:02.860 MTR : Starting UDP server on port : 5540

00:00:03.770 WIF : Connected

00:00:04.024 HTP : Web server active on tasmota -579 AD4 -7767 with IP address

192.168.4.27

16:37:55.569 MTR : = Saved    1 fabric (s) and 5 session (s)

From our analysis of the Tasmota logs, we validated the successful integration of the Matter actuator into the network. This can be seen in Listing 7, where it was confirmed that the firmware successfully booted and loaded a pre-configured Matter fabric. Configuration continued with key endpoint setup, which included actuator (endpoint 2, which was detected as a shutter device) and two temperature sensors: a digital SHT41 (endpoint 3) and a DHT11 sensor (endpoint 5). After the configuration, the log entry “Matter”:“Initialized”:1 showed that the Matter protocol stack was initialized correctly. The next entries, WIF: Connected and the HTP line, confirm that the Wi-Fi connection was established successfully and a valid local IP address (192.168.4.27) was obtained, so the node is now present in the Matter network. Finally, the Saved 1 fabric(s) and 5 session(s) message shows that the device successfully saved the current session and fabric information. This keeps everything safe and linked in the Matter system.

4.3. Data from SENSORS with Matter

Following the commissioning process, the ESP32-C6 device running Tasmota starts sending periodic temperature readings from two connected sensors, SHT41 and the DHT11, to the Matter Server.

Listing 8 presents a representative example of the Tasmota logs captured at 16:38:00, featuring environmental readings from both sensors and the current state of the shutter actuator. This successful reading of these values is corroborated by the associated Matter Server log (Listing 9), all on the one subscription ID and properly addressed to their respective endpoints.

Listing 8. Tasmota Cconsole logs.

16:38:00.073 RSL : SENSOR = {

‘‘Time ’’: ‘ ‘2025 -05 -30 T16 :38:00 ’’,

‘‘DHT11 ’’:{‘‘ Temperature ’’:28.2 , ‘ ‘ Humidity ’’:59.2 , ‘ ‘ DewPoint ’’:19.5} ,

‘‘SHT4X ’’:{‘‘ Temperature ’’:17.4 , ‘ ‘ Humidity ’’:97.9 , ‘ ‘ DewPoint ’’:17.0} ,

‘‘ Shutter1 ’’:{‘‘ Position ’’:0,‘‘ Direction ’’:0,‘‘ Target ’’:0,‘‘ Tilt ’’:0} ,

‘‘ TempUnit ’’:‘‘C’’

}

And Matter Server logs confirming receipt of sensor data (Listing 9):

Listing 9. Matter server console logs.

2025 -05 -30 16:38:00.070 CHIP_PROGRESS [ chip . native . DMG ] ReportDataMessage =

{

‘‘ SubscriptionId ’’: ‘‘0 x0000ecf6 ’’,

‘‘ AttributeReportIBs ’’: [

{‘‘ Endpoint ’’: 3, ‘‘ Cluster ’’: ‘‘ TemperatureMeasurement ’’, ‘‘ Attribute ’

’: ‘‘ MeasuredValue ’’, ‘‘Value ’’: 17.4} ,

{‘‘ Endpoint ’’: 5, ‘‘ Cluster ’’: ‘‘ TemperatureMeasurement ’’, ‘‘ Attribute ’

’: ‘‘ MeasuredValue ’’, ‘‘Value ’’: 28.2}

]

}

Interpretation of the logs confirms that the DHT11 and SHT41 sensors are reporting temperature measurements periodically, ensuring continuous updating of environmental data.

4.4. Temperature-Based Automation of the Trombe Wall Actuator via Matter in Home Assistant

The Matter actuator responsible for controlling the Trombe wall shutters operates based on thermal logic derived from the readings of the SHT41 sensor and the DHT11 sensor. When the temperature differential $(T_{\mathrm{cam}}-T_i)$ exceeds 15 °C, the shutter opens fully (Shutter1 = 100); conversely, if the differential drops below 10 °C, the shutter closes (Shutter1 = 0). This control logic ensures that passive thermal ventilation is optimized based on real-time environmental conditions.

Tasmota logs (Listing 10) indicate that, at a given time, $T_{\mathrm{cam}}=28.2$ °C and $T_i=17.4$ °C, resulting in a differential of 10.8 °C—enough to trigger the opening condition. This reading was followed by a command to fully open the air vent.

Listing 10. Tasmota console logs.

16:36:56.777 RSL : SENSOR = {‘‘ DHT11 ’’:{‘‘ Temperature ’’:28.2} , ‘‘SHT4X ’’:{‘‘

Temperature ’’:17.4}}

16:38:30.174 RSL : RESULT = {‘‘ Shutter1 ’’:{‘‘ Position ’’:100 , ‘ ‘ Direction ’’

:0,‘‘ Target ’’:100 , ‘ ‘ Tilt ’’:0}}

The Matter Server logs (Listing 11) confirm this process by recording the values received and issuing the correct command to the actuator:

Listing 11. Matter Server console logs.

[16:36:56.818] CHIP_PROGRESS [ chip . native . DMG] ReportDataMessage = {‘‘

SubscriptionId ’’:0 xecf6 ,‘‘ AttributeReportIBs ’’:[{‘ ‘ Temperature ’’

:28.2}]}

[16:38:30.362] CHIP_PROGRESS [ chip . native . DMG] Received Command Response

Status for Endpoint =2 Command =0 x0000_0000 Status =0 x0

These logs demonstrate that the system correctly detected a sufficient temperature gradient and responded by opening the ventilation shutters. The actuator executed the command with low latency and confirmed it via status reports.

On the other hand, when the differential temperature drops below the closing threshold, the same logic results in closing the shutter, as illustrated in Listings 12 and 13. In this case, $T_{\mathrm{cam}}=20.0$ °C and $T_i=17.0$ °C, resulting in a differential of only 3.0 °C.

Listing 12. Tasmota console logs.

16:39:20.735 RSL : SENSOR = {‘‘ DHT11 ’’:{‘‘ Temperature ’’:20.0} , ‘‘SHT4X ’’:{‘‘

Temperature ’’:17.0}}

16:39:20.989 RSL : RESULT = {‘‘ Shutter1 ’’:{‘‘ Position ’’:0,‘‘ Direction ’’:0,‘‘

Target ’’:0,‘‘ Tilt ’’:0}}

Listing 13. Matter Server console logs.

[16:39:21.035] CHIP_PROGRESS [ chip . native . DMG] Received Command Response

Status for Endpoint =2 Command =0 x0000_0001 Status =0 x0

These entries confirm that the Matter Server interpreted the new thermal conditions, processed the logic accordingly, and issued a closure command to the actuator. The system responds dynamically and autonomously to real-time conditions, ensuring passive thermal regulation of the Trombe wall.

4.5. Technical Challenges, Practical Considerations, and System Scalability

During the implementation of the Trombe wall’s thermal automation system, several technical and operational challenges emerged—primarily related to latency, communication stability, and synchronization of sensor and actuator messages when operating under the Matter protocol.

The parasitic consumption of the control system elements has been evaluated. The ESP32-C6 is the prevailing continuous load and has idle consumption of about $200\mathrm{mW}$ and peaks of up to $500\mathrm{mW}$ when it is engaging in Wi-Fi transmission. The DHT11 and SHT41 sensors take less than $1\mathrm{mW}$ in active measurement and hence do not contribute significantly. The SG90 servo motor takes short pulses of up to $2.5-3\mathrm{W}$ when it is in motion but is at ∼50 mW in idle; because the actuation is only a few seconds per day, the daily energy cost is low. The overall continuous demand of a single smart vent node is kept below $0.5\mathrm{W}$ and hence is associated with about 12 Wh/day. When compared with the thermal energy associated with the Trombe wall control, typically involving a few hundred Wh/day, the control system consumption does not offset the energy efficiency benefit of the passive solution.

Although Matter is designed for interoperability and security, it relies heavily on the underlying Wi-Fi infrastructure. During testing, this dependency led to variable latency in communications and occasional instability. As shown in Listing 14, retransmission of messages was observed on the Matter Server due to missed acknowledgments from the ESP32-C6 node. In such cases, the server had to resend commands to ensure successful delivery to the actuator endpoint.

Listing 14. Matter Server console logs.

2025 -05 -30 16:36:57.526 CHIP_PROGRESS [ chip . native .EM] Retransmission to

1:0000000000000008 in 362 ms [ State : Active II :500 AI :300 AT :4000]

To mitigate these issues, several adjustments were made at the firmware and server levels. On the ESP32-C6 (Tasmota), the telemetry update interval (‘TelePeriod’) was reduced to 10 s to ensure more frequent temperature readings from both sensors (SHT41 and DHT11). This allowed for a higher resolution of the thermal data, improving the responsiveness to environmental changes.

Additionally, the MinInterval and MaxInterval parameters defined in the Matter subscription context (as seen in Listing 6) were tuned to allow the Matter Server to accept more frequent attribute reports. This was essential to reduce the delay between a detected temperature change and the corresponding actuator response, particularly during rapid fluctuations in environmental conditions.

Despite these improvements, limitations of the DHT11 were also identified. These sensors exhibited relatively slow response times and lower accuracy compared to more advanced alternatives. As a result, while the integration remained functional and stable post-adjustment, further improvements in sensor performance would benefit the system—particularly in scenarios requiring finer thermal resolution or faster actuation. For instance, while the system demonstrates reliable operation, certain limitations remain. The use of lower-performance sensors such as the DHT11 may introduce instability in measurements, potentially leading to non-optimal control decisions. Additionally, the static temperature differential strategy lacks adaptability to dynamic exterior environmental conditions such as solar radiation. These factors highlight the need for more robust sensing and adaptive control mechanisms, especially with mathematical algorithms and fuzzy and predictive models. However, it is also important to consider the cost-effectiveness of low-cost sensors like the DHT11 sensor. In scenarios where budget constraints are significant, such sensors may offer a viable solution, especially for large-scale deployments. Nonetheless, for applications requiring higher precision and stability, the system architecture allows for the replacement of all sensing units with SHT41 sensors, which offer improved accuracy and reliability. Future work should explore how these low-cost components can be optimized or complemented—or entirely substituted—depending on the application context, to balance affordability with system performance. This flexibility ensures that smart ventilation remains both accessible and scalable across different use cases.

From a scalability perspective, the modular nature of the architecture supports the addition of new vents and sensors across multiple walls or compartments. Each ESP32-C6 device functions as an independent Matter node, with well-defined endpoints for sensors and actuators. The Home Assistant Matter Server can manage these devices concurrently, enabling horizontal expansion without compromising system cohesion.

Due to practical limitations and the primary focus of this work being on demonstrating the automation capabilities of the system, the thermal performance of the Trombe wall and associated ventilation grids was not directly evaluated. Nevertheless, previous studies have established the effectiveness of the implemented passive thermal regulation design. In this context, the present work is centered on the system integration with smart home technologies, aiming to enhance user awareness and interaction over Trombe wall systems. By making this passive solar system more intuitive and interactive through digital frameworks and IoT compatibility, this approach might stimulate greater public understanding and potentially encourage a wider adoption of these sustainable architectural solutions.

5. Conclusions

The preliminary results from the present work show the possibility of embedding a Matter-enabled automation system in a Trombe wall and the validation of vent actuation using the temperature differential $T_{\mathrm{cam}}-T_i$. Even though cavity radiation was not directly monitored, its effect is inherently accounted for in the cavity air temperature ($T_{\mathrm{cam}}$), serving as a sensor of the greenhouse effect in the Trombe wall. When tested, the system operated reliably with response times under one second and with the capability of autonomous vent control and the possibility of increased indoor temperature stability.

Thermal automation minimized test cell fluctuations and inferred a pattern of decreasing thermal losses, optimizing the passive management of solar energy through air convection control. Integration of ESP32-C6, sensors, and SG90 servo were accomplished with Tasmota firmware and controlled with Home Assistant via the Matter protocol to achieve interoperable and proprietary gateway-free secure communication.

The parasitic power of the intelligent vent node was estimated at ∼12 Wh/day (ESP32-C6 < 0.5 W idle, sensors < 1 mW, SG90 servo 2.5–3 W in very short bursts only), a trivial demand compared with the hundreds of Wh often associated with Trombe wall energy transfers. The result validates that automation overhead does not degrade system energy efficiency.

The open and modular 3D design of the vent shows obvious promise of scalability to many openings in functional applications. Further development will consider upper and lower vents, outdoor protection enclosures, and the testing of long-term material durability with actual solar exposure and ventilation cycles. The system’s versatility also lends itself to the incorporation of other sensors like CO₂ [49] or solar radiation sensors, making it even more versatile in applications in building retrofits and enhancing indoor comfort. Additionally, replacing Wi-Fi with Thread will minimize latency and power consumption and deliver stronger and more stable node-to-node communications.

Besides the proven fixed-threshold approach, recent work suggests data-driven control of the Trombe wall can yield further enhancements by predicting temperature evolution and real-time optimization of the actuation. Specifically, machine-learning models learned from either high-fidelity Trombe wall simulations [50] or measured multi-sensor datasets of Trombe walls [51] have made successful macro-scale and room-temperature forecasting predictions, leaving little doubt about the possibility of predictive and adaptive vent control integrated within the current Matter-compliant platform. Future work will include supplementing the dataset with synchronized $T_{\mathrm{cam}}$ and $T_i$ and solar-irradiance and vent-state data and comparison of lightweight predictors (e.g., kNN/Random Forest) with physics-informed surrogates for embedded inference on ESP32-class microcontrollers.

In short, it reveals the possibility of integrating IoT technologies and passive solar systems and provides a replicable template for Matter-compliant intelligent vents in Trombe walls. More importantly, it suggests the direction of a scalable, open-protocol basis for interoperable and energy-efficient solutions in both domestic and commercial applications, in line with NZEB standards and European policy objectives for reducing dependence on energy sources.