Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics.

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.

The efficient recovery of waste heat is essential for improving sustainability in industrial and urban energy systems. This study presents the experimental evaluation of a passive heat recovery unit composed of finned thermosyphons and Bismuth Telluride (Bi₂Te₃) thermoelectric generators (TEGs). The primary objective was to characterize its simultaneous thermal recovery and electrical generation capabilities under airflow and temperature conditions simulating low-grade industrial exhaust streams. The system was tested in an open-loop wind tunnel simulating exhaust gases under air velocities of 0.6, 1.1, and 1.7 m/s. Heat was transferred to the TEGs through finned thermosyphons, enabling power generation via the Seebeck effect. The passive heat exchange mechanism successfully recovered up to 250.9 W of thermal power, preheating the inlet air by a maximum of 9.5 °C with a peak thermal effectiveness of 44.4%. Simultaneously, the system achieved a maximum temperature difference of 30.0 °C across the thermoelectric modules, generating a total electrical power of 163.7 mW (81.8 mW per TEG). This dual-purpose operation resulted in a maximum overall first-law efficiency of 9.38% and an electrical power density of 52.20 W/m² from the low-grade thermal stream. These results confirm the technical feasibility of this compact, passive, and maintenance-free design, highlighting its potential for integration into applications like district heating or industrial ventilation, where balancing thermal and electrical outputs is crucial.

In the present work, an experimental analysis was performed to obtain the calibration curve of three load cells connected in series. The control of the load applied on a given component is an important factor in some engineering applications, for example, in cases where it is desired to increase the heat exchange between two surfaces. One of the ways to control the applied load is the use of load cells, which has as its principle of use a strain gauge that has its resistance varied when it undergoes a deformation, thus causing a voltage variation due to the application of load. This study used an ArduinoTM microcontroller as a data acquisition system and blocks with known mass for load application. In this way it was possible to obtain the calibration curve of the load cells by means of linear regression between the mass of the blocks and the data obtained by the ArduinoTM microcontroller and verify their applicability according to their measurement uncertainties.

Buildings account for a significant portion of global energy consumption, estimated at 30-40%, and also contribute to greenhouse gas emissions. Energy consumption in a building is mainly thermal (natural gas) and electrical. This energy is usually used for heating water, cooking, illumination, ventilation and air conditioning, powering appliances, floor heating, and other activities. These activities were examined, and their substitution by solar-based energy sources was reviewed. To achieve this objective, an extensive literature review across Scopus, Direct Science, and Web of Science in relevant areas, including building energy needs, thermal and visual comfort, and construction materials and components, was conducted. Innovative construction materials, including mortars, bricks, concrete, and components such as Trombe walls, can enhance thermal efficiency and thermal comfort. Solar energy can replace fossil-based energy for the provision of hot water, and hot fluid for air conditioning absorption chillers systems. Building components such as thermally efficient windows (double-glazed, evacuated, etc.), bright windows, and facades can help maintain a thermally and visually comfortable indoor environment. Electric energy for buildings’ services, such as illumination, ventilation, and other services, can be provided by solar PV panels. The review shows that solar energy can significantly contribute to decarbonizing buildings’ energy needs, maintaining passive thermal and visual comfort, and reducing emissions. The review indicates that a solar air conditioner with a 12,000 BTU cooling capacity can save 8-28% of energy and reduce emissions by 7.74-28.27%. It also showed that selecting windows and facades is a critical issue. A comparison of the energy-saving of thermo-chromatic, double-glazed, and clear glass windows indicated a reduction of 8.91% to 10.96% in energy consumption due to double-glazed windows and a reduction of 20.22% to 24.19% due to thermo-chromatic windows. Smart windows with photovoltaic electrochromic (PV-EC) enabled a useful daylighting illumination of about 75.26% and energy saving of about 15.79% compared to ordinary windows. It is recommended that applications such as hot water, water distillation, illumination, electricity, and air conditioning be powered by solar energy. In the construction of buildings, thermally efficient materials and components should be prioritized. To promote building decarbonization, it is necessary to reduce the cost of materials, create financial incentives and low-interest grants for retrofitting, and create public policies to promote solar-based energy applications in buildings.