Browsing by Author "Pacheco Portilla, Mario Gustavo"

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Evaluación de la eficiencia de paneles solares como sistema de captación de energía para edificaciones del área urbana de Cuenca
(2017) Izquierdo Torres, Ismael Fernando; Pacheco Portilla, Mario Gustavo; Zalamea León, Esteban Felipe; Gonzalez Morales, Luis Gerardo
This study assesses the efficiency of solar photovoltaic panels as energy systems for the Centro Histórico of Cuenca. First, the efficiency of monocrystalline solar panels was assessed by in-situ measurements during December 2016 and three days of January 2017, for frequent inclinations and orientations of the Centro Histórico’s typical roofing. Second, a 21-day evaluation of efficiency loss by dirt accumulation was carried out during January 2017. Third, a linear regression between a TRNSYS power simulation and in-situ measurements was performed. Fourth, a technical and economic evaluation of photovoltaic systems was conducted for two buildings of the Centro Histórico as well as an environmental analysis, considering the energy demands of four zones of the Centro Histórico. Results show that the optimal configuration is East at 14° with an average efficiency of 13.33 %, higher than the least favorable configuration, North at 18.26°, by 18.2 %. The second evaluation showed an average efficiency loss of 2.77 % for a 14-day period of dirt accumulation. Moreover, a R2 = 0.528 from the linear regression showed that the TRNSYS simulation explains more than half of the variation of the in-situ measurements. Technical and economic analysis revealed that the building with higher energy consumption presents a higher profitability. Additionally, the environmental analysis showed that emissions between 644.15 and 683.91 tCO2eq/year could be avoided by using photovoltaic panels. This study demonstrates that the incorporation of photovoltaic technologies in the Centro Histórico of Cuenca is feasible and profitable.
Prediction of imports of household appliances in Ecuador using LSTM networks
(Springer Nature Switzerland AG 2020, 2020) Tello Guerrero, Marco Andrés; Izquierdo Torres, Ismael Fernando; Pacheco Portilla, Mario Gustavo; Vanegas Peña, Paúl Fernando
Time series forecasting is an important topic widely addressed with traditional statistical models such as regression, and moving average. This work uses the state-of-the-art Long Short-Term Memory (LSTM) Networks to predict Ecuadorian imports of Home Appliances, and to compare the results against those obtained by traditional methods. First, an ARIMA model was used to forecast imports data. Then, the predictions were calculated by a Univariate LSTM network. The time series used in both experiments was the monthly average of imports from 1996 to April 2019. In addition, time series of GDP Growth, Population, and Inflation were included in the model to test prediction improvements. The performance of the models was assessed comparing the Mean Squared, Root Mean Square and Mean Absolute Error metrics. The results show that a LSTM network produces a better fit of the imports data and improved predictions compared against those produced by the ARIMA model. Furthermore, the use of multivariate time series (i.e., GDP Growth, Population, Inflation) data, for the LSTM model, did not produce significant improvements compared to the univariate imports time series.
Simulación fotovoltaica considerando parámetros de integración en edificaciones
(2019) Izquierdo Torres, Ismael Fernando; Pacheco Portilla, Mario Gustavo; Gonzalez Morales, Luis Gerardo; Zalamea León, Esteban Felipe
This research calibrates and validates a model for monocrystalline photovoltaic systems in SAM (System Advisor Model) for power generation simulation, considering the meteorological characteristics of Cuenca, Ecuador, close to the equatorial line. The electrical performance is calculated by arranging photovoltaic systems with specific characteristics, with inclinations that respond to conventional local roofing and different orientations. Efficiency is calculated with in-situ measurements over a period of 18 days. Meteorological data were used to calibrate a weather file for the year 2016. Annual yields are estimated according to inclination and orientation, and technical characteristics of the photovoltaic system. Losses are detected due to dirt accumulation and increase in temperature of the panels. The model is validated by linear regression, by comparing the simulated values with the data obtained from in-situ measurements of a reference panel deployed horizontally. The results show an average efficiency loss of 2,77% for dirt conditions and up to 30% for temperature increases.