Conference Agenda

Airborne diseases pose a significant risk to public health, especially in enclosed spaces with poor ventilation. In this context, the present research aimed to evaluate the application of UVC LED technology in an air purifier to improve the inactivation of pathogens. The proposed system integrated an ESP32 microcontroller, particulate matter and carbon monoxide sensor, and IoT connectivity for real-time data monitoring and storage via the Firebase platform. During the experimental phase, the survival of MS2, Qβ, and φX174 viruses was evaluated, determining inactivation rate constants and the doses required for their reduction. The results showed a significant decrease in viral load after exposure to UVC radiation, with φX174 exhibiting greater sensitivity compared to the other microorganisms evaluated. Furthermore, the purifier stabilized air contaminant levels in a short time through the automatic activation of the ventilation and disinfection system.

Residential energy consumption has skyrocketed in recent years, particularly in Latin America, where electricity rates are variable and complex, difficult for users to understand, which leads to misinformation and numerous complaints about excessive charges. Considering this situation, this study proposes the design of a real-time commercial electricity rate calculator that allows for the monitoring and forecasting of energy and economic consumption in households. The research was quantitative, applied, and experimental in design, involving 20 homes selected by non-probabilistic sampling in the city of Lima. Surveys, observation sheets, and technical tests were used, validated by expert judgment and Cronbach's alpha. The results revealed improvements in consumption reading capacity (from 2.1 to 4.6 points), in the frequency of consultation and in the detection of unnecessary consumption (90% of users), and a high rating of accuracy, ease of use, and reliability of the device. Therefore, the system developed represents a technological solution to empower users to manage their electricity consumption, promoting savings and improving the transparency of the residential tariff system.

This work evaluates the performance of the SARIMA model in forecasting short-term (daily) electricity demand in the Peruvian electricity system, highlighting its ability to capture seasonal patterns and intraday variations inherent in load behavior. By incorporating autoregressive, differentiation, and seasonal components, the model adequately reproduces daily fluctuations, generating estimates that are consistent and coherent with the historical dynamics of the series. Likewise, the results demonstrate the robustness, interpretability, and computational efficiency of the proposed approach, making it a competitive alternative to more complex methods. Thus, the SARIMA model is positioned as a competitive tool for operational planning and short-term decision-making processes within electric power systems.

The liberalization of electricity markets has created opportunities for consumers to reduce costs and access more sustainable energy sources, while also raising new regulatory and operational challenges. This paper evaluates the feasibility of migrating a qualified consumer in Brazil from the regulated contracting environment to the deregulated electricity market. For this purpose, a digital platform was developed to automatically extract and manage data from electricity bills, enabling simulations based on two real proposals offered by energy traders. Results show potential savings between 22.2% and 28.2% compared to the regulated market, representing significant longterm economic benefits while ensuring the purchase of renewable energy. Additionally, the paper discusses the broader implications of market liberalization in Latin America by analyzing the current regulatory and infrastructural situation in Honduras. The findings highlight both the opportunities for efficiency and competitiveness and the regulatory barriers that must be addressed for an effective transition in developing economies

An intelligent Maximum Power Point Tracking (MPPT) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS), trained using real irradiance and temperature data, is proposed and experimentally validated for a photovoltaic system operating under highly variable climatic conditions.

Unlike most approaches reported in the literature, which rely on synthetic datasets and deterministic real-time platforms, this work integrates a double-diode photovoltaic model with field measurements acquired using a Class A pyranometer to construct a training dataset that captures the stochastic nature of tropical environments.

The controller is implemented on a Raspberry Pi 5 running a Linux operating system and governs a 100 kHz DC–DC Boost converter connected to a 340 W photovoltaic array installed in Cúcuta, Colombia. The ANFIS estimates the optimal operating voltage based on irradiance and temperature input vectors.

Experimental results demonstrate a tracking time of 100 ms compared to 240 ms achieved by the conventional Perturb and Observe (P&O) method, reaching a maximum steady-state efficiency of 96.4% and a dynamic efficiency of 94.5% under real fluctuating irradiance conditions.

These results confirm that intelligent MPPT controllers trained with real data can operate robustly on low-cost, non-deterministic embedded platforms, enabling their deployment in practical photovoltaic microgrid applications.

This article describes the design, manufacture, and evaluation of a prototype grain mill powered by a solar photovoltaic energy system. The mechanical structure was constructed using square steel tubes, following cutting, beveling, and welding processes with 6011 electrodes to ensure rigidity and stability. The equipment integrates an adapted manual mill and a washing machine motor with pulley transmission, which was assembled taking into account alignment, fastening, and coupling using an intermediate shaft. The testing phase included measurements of voltage, current, and power both at no load and during grinding, using a multimeter and a wattmeter to corroborate the values provided by the inverter. The results showed that the prototype far exceeded the proposed capacity, grinding 4 kg of corn in less than 10 minutes with an energy consumption of 0.214 kW per hour, verifying that the photovoltaic system provides autonomy and operational stability and a return on investment of 4.38 years. In conclusion, the mill developed is technically, economically, and environmentally viable as a sustainable alternative to improve agricultural productivity in the Arequipa region.

Digital revolution is altering industrial production as supply-chain disruption, volatility, sustainability limits, and increased complexity overwhelm centralized automation. IoT-enabled cyber-physical systems that integrate sensors, analytics, and distributed control are expanding smart manufacturing by strengthening autonomy, resilience, and scalable optimization. This study suggests an integrative architecture that combines IoT sensing, AI-driven inference, Digital Twins, and edge-enabled control inside a CPS framework developed for developing nations. The method aligns component intelligence with system goals by using shared data semantics, explicit control allocation, and federated twin synchronization rather than separate modules. The findings indicate how, even with heterogeneous infrastructure and limited observability, introducing inference and twin updates into control loops decreases the gap between analytics and operational actuation. Additionally, by prioritizing sustainability and resilience in design, the strategy improves deployment viability and transferability. All things considered, the study provides an architecture-level foundation for Industry 4.0 acceptance and Industry 5.0 development in resource-constrained settings

The analysis of the performance of overhead power lines against the direct impact of lightning on phase conductors is a topic of interest in the field of transmission lines. The shielding failure flashover rate (SFFOR) measures this performance. The SFFOR is calculated after solving a set of complex equations, which may result in unintentional errors and require excessive computation time if the calculations are performed manually. In this study, a graphical user interface (GUI) was developed to compute the SFFOR. In contrast to existing similar tools, the developed GUI allows for the modelling of the probability density of the lightning current magnitude from historical data. In addition, the GUI can determine the optimal shielding angle. The equations for the SFFOR computation were obtained from the standard IEEE-1410 and -1253 and brochure 063 of CIGRE. The application of the proposed methodology to a 33 kV overhead power line located at 2700 m.s.n.m. indicated an SFFOR of 0.32 and 0.00 flashovers/100 km-year. The first result was obtained using the parameters of the log-normal function provided in the brochure 063. The second result considered the parameters of the distribution function determined by the proposed method. This shows the sensitivity of the results to the lightning current modelling. Both values were obtained in less than a second after entering the data into the GUI. Therefore, the developed GUI facilitates the analysis of the performance of power transmission shielding, allowing the evaluation of possible shielding improvements in a short time.