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Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms
(ELSEVIER, 2022-06-01) Serrano-Lujan, Lucía; Toledo, Carlos; Colmenar, Jose Manuel; Abad, Jose; Urbina, Antonio
Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure’s skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days).
Environmental impact of the production of graphene oxide and reduced graphene oxide
(SPRINGER NATURE, 2019-01-25) Serrano-Lujan, Lucía; Víctor-Román, Sandra; Toledo, Carlos; Snahuja-Parejo, Olga; Mansour, Ahmed; Abad, Jose; Amassian, Aram; Benito, Ana M.; Maser, Wolfgang; Urbina, Antonio
Reduced graphene oxide (rGO) is widely seen as the most promising route for the low-cost mass production of graphene for many applications ranging from ultrathin electrodes to structural nanocomposites. The Hummers and Marcano methods are the two most successful approaches for producing high-performance rGO, but have been criticized for producing toxic emissions. We have applied life cycle assessment methodology to evaluate the environmental impacts of both production routes for GO and rGO in the context of applications requiring bulk materials or thin coatings. We find no current obstacle to the industrial scale production of graphene arising from its environmental impact. The cumulative energy demand is found to have a cap value between 20.7 and 68.5 GJ/Kg, a relatively high value; impact in other cat- egories (such as human toxicity or resource depletion) is lower, and materials inventory does not include critical/strategic materials other than graphite itself. Our study proposes 1 kg of graphene as functional unit, and an application-specific functional unit normalized by conductivity which show that Hummers production method is far more suitable for bulk applications of graphene, with lower embedded energy per kg of graphene production, while Marcano’s production method is better suited for thin film electronic applications.
Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization
(MDPI, 2019-02-19) Toledo, Carlos; Serrano-Lujan, Lucia; Abad, Jose; Lampitelli, Antonio; Urbina, Antonio
Photovoltaic electricity generation is growing at an almost exponential rate worldwide, reaching 400 GWp of installed capacity in 2018. Different types of installations, ranging from small building integrated systems to large plants, require different maintenance strategies, including strategies for monitorization and data processing. In this article, we present three case studies at different scales (from hundreds of Wp to a 2.1 MWp plant), where automated parameter monitorization and data analysis has been carried out, aiming to detect failures and provide recommendations for optimum maintenance procedures. For larger systems, the data collected by the inverters provides the best source of information, and the cross-correlated analysis which uses these data is the best strategy to detect failures in module strings and failures in the inverters themselves (an average of 32.2% of inverters with failures was found after ten years of operation). In regards to determining which module is failing, the analysis of thermographic images is reliable and allows the detection of the failed module within the string (up to 1.5% for grave failures and 9.1% of medium failures for the solar plant after eleven years of activity). Photovoltaic (PV) systems at different scales require different methods for monitorization: Medium and large systems depend on inverter automated data acquisition, which can be complemented with thermographic images. Nevertheless, if the purpose of the monitorization is to obtain detailed information about the degradation processes of the solar cells, it becomes necessary to measure the environmental (irradiance and ambient temperature), thermal and electrical parameters (I-V characterization) of the modules and compare the experimental data with the modelling results. This is only achievable in small systems.

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Examinando Investigación por Autor "Abad, Jose"