- ÍtemAcceso AbiertoConstrucción de un medidor de agua inteligente mediante Arduino para reducir las pérdidas en la distribución de agua(Universidad Católica de Cuenca., 2024) León Lazo, Rolando Israel; Macancela Sumba, Juan Gabriel; Morales Jadán, Diego Xavier; 0107204083; 0302304076This paper shows the use of Internet of Things (IoT) technology specifically applied to drinking water distribution systems, aiming to improve the efficiency of this service in the distribution network control segments and reduce the response time to problems such as service interruption, errors in the measurement readings, and losses of the vital liquid. In this context, the IoT technology applied to the ESP 32 module, flow sensor, and YF-S201 —a shut-off valve for remote monitoring and control of drinking water— is used, leading to better management of the vital resource and a more adequate and reliable reading, which will have a direct impact on the distributing company's and the consumer's economy. In addition, the customers will be able to interact with a Telegram Bot directly on their cell phones, in which they can ask questions about their consumption at any time of the day and receive a notification every 24 hours through the bot of their daily or monthly consumption.
- ÍtemAcceso AbiertoDiseño e implementación de un sistema de control para un bote abastecido por energía solar fotovoltaica(Universidad Católica de Cuenca., 2024) Castillo Pinos, Byron Wilfrido; Quinde Gómez, Cristian Joel; Icaza Alvarez, Daniel Orlando; 0302599253; 0302471354The increase of renewable energies, especially photovoltaic systems, is becoming more significant every day for the supply of energy in a self-sustainable and environmentally friendly way. This research is based on a solar-powered boat owned by the Catholic University of Cuenca, initially operated manually using physical force. First, the simulation results of the fuzzy control system are presented. As a second point —emphasizing the main objective and the secondary ones— the work also shows the creation of an application developed in the online software App Inventor. This is a freely available application for Android devices, supported by a microcontroller —in this case, a Raspberry Pi 3 was used—; it was possible to implement an automatic system so that, through the mobile device, the speed and rotation of the sustainable boat can be controlled, making it easily accessible by simply pressing a group of buttons. Additionally, it was considered that the boat could operate in its original form, i.e., manually if the system presents connection failures, burnt electronic elements, or any other issues that would prevent its correct operation once switched to automatic mode.
- ÍtemAcceso AbiertoAplicación del Método estadístico para el análisis y pronóstico de perfiles de consumo de energía eléctrica. Caso de estudio CIITT en la UCACUE(Universidad Católica de Cuenca., 2023) Beltrán Crespo, Henry Paul; Morales Ortega, Jhostin Andrés; Icaza Alvarez, Daniel Orlando; 0106742554; 0350030805In The present study represents a significant approach to the analysis of electrical consumption in the facilities of the Center for Research, Innovation, and Technological Transfer (CIITT by its Spanish acronym) at the Catholic University of Cuenca. The goal is to thoroughly analyze consumption profiles using statistical models of linear regression and multiple linear regression. With this approach, the aim is to anticipate and prevent potential future difficulties in the electrical system, leading to an effective optimization of resources and a significant improvement in the efficiency of all electrical consumption sectors. The database for this research spans a period of two months, from May to July, allowing the collection of valuable information on electrical consumption patterns at the CIITT. This data will be gathered exhaustively and accurately to obtain reliable and representative results of electrical consumption habits in the facilities. After analyzing the obtained data, improving the electrical energy consumption routine at the CIITT is advisable, seeking a more efficient use of energy and ultimately achieving significant cost savings in electrical consumption. This study aims to demonstrate the possible values of electrical energy consumption through statistical methods.
- ÍtemAcceso AbiertoPrevisión espacio temporal de la demanda eléctrica en la Empresa Regional Centro Sur(Universidad Católica de Cuenca., 2023) Macao Piña, Christian Andrés; Icaza Alvarez, Daniel Orlando; 0105782023This degree work presents the development and application of a spatial and temporal electric demand projection model through the creation of an algorithm in Machine Learning (ML) to respond to the medium- and long-term planning problem of the electric distribution system of the Regional Electric Company (Centro Sur C.A. in Spanish.) This study will provide a positive benefit for the electric service distribution company in terms of planning and allocation of resources in the distribution area. This research is focused on the projection of the electric demand in small areas disaggregating the global projection of the Regional Electric Company (CENTROSUR) considering a study area that has eight substations (Electrical Substations), the simulation model will analyze the different types of consumers such as residential, commercial, and industrial. The model applied in this research is developed with convolutional neural networks (CNN) using Python programming language and considering input variables such as spatial factors, consumer growth projection, and electricity demand projection. A map of growth probabilities in the study area was created. Then, the mathematical model cellular automaton (CA) was used to assign new customers spatially, according to the global projection of each substation.
- ÍtemAcceso AbiertoEstudio para incorporación de inteligencia artificial en circuitos de transformadores de distribución para análisis de demanda, enlace y análisis de información(Universidad Católica de Cuenca., 2023) Molina Farez, Diana Dennise; Siguencia Siguenza, Oscar Mauricio; 1400770127This research focuses on the development of a thermal image prediction model that classifies images according to their degree of thermal degradation severity. The significance of this classification has been highlighted for the early detection of problems and decision-making in several fields. The methodology used in this study was based on collecting a dataset of thermal images with different degrees of thermal degradation. Image processing techniques were applied, and a deep learning algorithm was used to train and evaluate the prediction model. The results obtained showed that the developed prediction model was capable of classifying thermal images according to their degree of thermal degradation severity with an acceptable accuracy. A substantial evaluation was observed between the thermal characteristics of the images and the degree of thermal degradation. In conclusion, this study demonstrated the feasibility of developing a thermal image prediction model for classifying the severity of thermal degradation. However, some limitations were identified, including the need for a more diverse dataset and the exploration of more advanced image processing techniques. It is recommended to continue research and improve the prediction model, which includes collecting a more extensive and diverse dataset. Furthermore, it is suggested to explore more advanced image processing techniques and consider the integration of other relevant variables to improve the accuracy of the model.