Conference Agenda

The objective of this study was to determine the effect of an IoT system with incentive mechanisms on residential electricity consumption control. The research had a quantitative approach and a longitudinal pre-experimental design, applied in 10 homes in the city of Cajamarca, Peru, where 30 electrical appliances were monitored. Data collection was carried out in two phases: a 60-day pre-test stage without intervention and a 60-day post-test stage with the IoT system implemented. The results showed an average reduction in daily electricity consumption of 19.9% (kWh/day), a decrease in peak power demand of 15.4% (W), a reduction of 7.7% in daily hours of use, and a decrease in estimated monthly cost of 20.6%. The system also allowed for accurate monitoring of electrical parameters (V, A, W, kWh) and the effective activation of incentive mechanisms based on the percentage reduction in consumption relative to the individual baseline. Statistical analysis using the Student's t-test for related samples confirmed statistically significant differences (p < 0.05) between pre-test and post-test values. It is concluded that the IoT system with incentive mechanisms has a positive and significant effect on the control of residential electricity consumption.

This study presents an experimental characterization of the soil temperature profile in the city of Soyapango, El Salvador, performed at an educational institution during the summer season. The objective is to evaluate, from a thermal behavior perspective, the potential applicability of low-enthalpy geothermal systems, specifically Canadian-type earth–air heat exchangers, under local urban conditions.

Temperature measurements were performed at multiple depths between the surface and 3 m, and at different times of the day over a one-week period, to analyze thermal gradients, daily variability, and the degree of temperature attenuation with depth. The results show a significant reduction and delay in temperature fluctuations as depth increases; however, at 3 m depth the subsurface temperature still exhibits noticeable variability, particularly under high ambient temperature conditions typical of volcanic soils with low moisture content.

These findings suggest that, in similar geological contexts, greater depths or additional design considerations may be required to achieve the thermal stability necessary for effective passive air-conditioning. The study highlights the importance of local, in-situ thermal measurements as a preliminary step for the design of shallow geothermal systems in tropical urban environments.

Green hydrogen has emerged as a strategic option for decarbonizing the industrial sector, given its potential to substantially reduce emissions from energy use and carbon-intensive processes. Accordingly, this systematic review aimed to identify and classify the technologies, strategies, recent advances, and challenges reported in the literature, providing a comprehensive and up-to-date overview of green hydrogen production, storage, and transport while synthesizing evidence from an integrated value-chain perspective that is often addressed in a fragmented manner. A PICO-based search was conducted in the Web of Science database, and study selection followed the PRISMA framework using predefined inclusion and exclusion criteria. From 3,734 records initially retrieved, 65 studies met the eligibility requirements and were analyzed in depth. The results indicate that alkaline electrolysis and proton exchange membrane (PEM) electrolysis are currently the most mature production routes. For storage, underground salt caverns emerge as the most viable option for large-scale applications. For transport, chemical carriers and dedicated pipelines stand out as relevant alternatives depending on the scenario assessed. The most frequently reported challenges include high upfront investment costs, material degradation, hydrogen embrittlement, insufficient infrastructure, and the lack of harmonized regulatory standards. Overall, green hydrogen is positioned as a key energy vector to enable lower-impact industrial pathways; however, large-scale deployment will require improving system efficiencies, reducing costs, expanding storage and distribution networks, and establishing robust regulations to ensure safety, competitiveness, and long-term sustainability.

The reliable determination of dead oil viscosity directly influences transportation planning and reserve estimation within the Oriente Basin. Based on this need, the objective was to develop a mathematical simulation model to predict dead oil viscosity in wells of the Ecuadorian Amazon Basin. The study was conducted using an experimental, quantitative, and cross-sectional approach. PVT tests from ten Amazonian wells were analyzed, Pearson correlation coefficients were used to identify relevant predictor variables, and a multiple linear regression model was fitted using R software. The results were then compared with laboratory measurements. The findings show that reservoir temperature and API gravity exhibit significant inverse correlations of −0.76 and −0.86, respectively. These variables were integrated into the proposed model, which achieved a coefficient of determination (R²) of 0.8 and a reduction in mean error to 19.8%. In contrast, other evaluated models showed deviations of 81.6%, 40.1%, and 94.7%. In specific cases, such as the SUSHUFINDI-51 and YNNA-009 wells, the discrepancy was reduced to 6.8%, while traditional methods exceeded 500% errors under high-temperature conditions, highlighting the need to adjust predictive expressions to local contexts. Finally, it is concluded that the developed equation facilitates faster diagnostics and contributes to optimizing the design of pipelines and surface equipment.

Energy vulnerability in High-Andean dwellings necessitates the development of efficient, indigenous thermal insulation solutions. This study characterizes the thermal conductivity of Stipa Ichu fiber using a geometrically corrected, reduced-scale Hot Box method. To mitigate the metrological uncertainty inherent to low-cost devices, a hybrid experimental-numerical (CFD) methodology was implemented. Analysis using the SST k- turbulence model revealed that scale reduction induces significant multidimensional heat fluxes at the edges; consequently, a shape efficiency factor of 𝜂 = 0.47 was established. Upon applying this systematic correction, an effective thermal conductivity of was determined for a compaction density of 150 kg/m³. These results validate Stipa Ichu fiber as a competitive alternative to conventional insulators, providing a viable technical solution for mitigating extreme cold conditions

This research presents a detailed statistical analysis of daily energy production variance for the Carhuaquero Photovoltaic Solar Plant, a 565.25 kW small-scale solar installation operated by KONDU SAC in Peru. Using a complete annual dataset spanning from May 17, 2024, to May 16, 2025, this study examines the temporal variability and statistical distribution characteristics of photovoltaic energy generation. The analysis encompasses 365 consecutive daily observations, revealing significant insights into production patterns and operational efficiency metrics. Statistical modeling techniques were applied to characterize the probability distributions governing daily energy output, with particular emphasis on variance analysis and distribution fitting methodologies. The results demonstrate that daily energy production exhibits considerable variability, with values ranging from 2.5 to 6.1 MWh per day, indicating a coefficient of variation exceeding 25%. Seasonal patterns were identified through monthly aggregation analysis, showing distinct performance variations correlated with meteorological conditions typical of the Andean region. Distribution modeling revealed that energy production follows a near-normal distribution with slight positive skewness during peak production periods. The findings contribute to enhanced understanding of small-scale photovoltaic system performance characteristics and provide valuable insights for energy forecasting models and grid integration strategies. These results support improved operational planning and maintenance scheduling for similar solar installations in comparable geographical and climatic conditions.

Worldwide, industries use different toxic substances containing heavy metals, which end up in waters, representing a danger to public health and the environment; due to this, the present research aimed to evaluate the bioremediation capacity of the fungal species Rhizopus sp. from wastewater from a tannery in the city of Trujillo, Peru. A 3^k factorial design was applied, with two factors (k=2): initial concentration of Cr⁺⁶ and number of Rhizopus sp. roasts. As dependent variables, the percentages of removal of Cr⁺⁶, BOD₅, electrical conductivity (EC) and turbidity (NTU), as well as the final pH of the solution were measured. It was found that the two factors have a significant effect on the percentage removal of Cr⁺⁶, BOD5, electrical conductivity and turbidity, as well as on the final pH. An initial concentration of Cr⁺⁶ 0.05 ppm and 3 roasts of Rhizopus sp. are recommended. to optimize bioremediation of wastewater from tanneries contaminated with Cr⁺⁶.