Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

Diseño e implementación de un sistema domótico utilizando reduced instruction set computing y servidor de aplicaciones

TesisLa automatización de las viviendas proponen mejorar cuatro aspectos fundamentales de las mismas, la seguridad, el ahorro energético, la accesibilidad y el confort; nosotros hemos puesto principal importancia en la seguridad y el ahorro energético. En la actualidad existen una gran variedad de sistemas y tecnologías disponibles que pueden ser utilizados y permiten obtener las prestaciones antes mencionadas tales como: HEYU, HCS, ExDomus, X10, etc. Sin embargo, se decidió trabajar con la tecnología web, teniendo como base del hardware del servidor un computador con arquitectura Reduced instruction Set Computing y como software de este un servidor LAMP. El sistema consta de actuadores y sensores que se comunican con el servidor a través de una tarjeta de control utilizando la tipología centraliza y la topología de red estrella que hacen que el servidor reciba toda la información proveniente de los sensores para que este las procese y envíe las ordenes correspondientes a los actuadores. Se hicieron análisis comparativos en cuanto al consumo energético de nuestro servidor con respecto a servidores tradicionales logrando obtener resultados exitosos. La reducción en el consumo energético respecto a uso de un servidor tradicional con procesador Xeon E3-1200 fue considerable, lográndose en una reducción de hasta 445 watt

Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Level

The production of solar energy at altitudes higher than 3800 m above sea level is not constant because the relevant factors are highly varied and complex due to extreme solar radiation, climatic variations, and hostile environments. Therefore, it is necessary to create efficient prediction models to forecast solar production even before implementing photovoltaic systems. In this study, stacking techniques using ElasticNet and XGBoost were applied in order to develop regression models that could collect a maximum number of features, using the LASSO, Ridge, ElasticNet, and Bayesian models as a base. A sequential feature selector (SFS) was used to reduce the computational cost and optimize the algorithm. The models were implemented with data from a string photovoltaic (PV) system in Puno, Peru, during April and August 2021, using 15 atmospheric and photovoltaic system variables in accordance with the European standard IEC 61724-20170. The results indicate that ElasticNet reduced the MAE by 30.15% compared to the base model, and that the XGBoost error was reduced by 30.16% using hyperparameter optimization through modified random forest research. It is concluded that the proposed models reduce the error of the prediction system, especially the stacking model using XGBoost with hyperparameter optimization