An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.This research has been partially supported by the Spanish Consejería de Economía, Innovación y Ciencia of the Junta de Andalucía under projects TIC-6441 and P11-RNM7115, and the Spanish MEC under project ECO2011–29751

Caracterización y generación de series de exposición horaria de radiación global

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Atómica, Molecular y Nuclear, leída el 06-04-1995El presente trabajo ha estudiado y caracterizado las series de exposición horaria con objeto de predecir los valores de irradiancia solar horaria en aquellos puntos donde no se disponga de datos meteorológicos fiables. El hecho de estudiar las series horarias ha sido debido a que muchas de las aplicaciones de la energía solar necesitan no solamente valores medios diarios de la irradiancia, sino que, por sus características particulares de funcionamiento, es preciso conocer la variabilidad de la irradiancia solar a lo largo del día. De esta forma, trabajando con datos horarios es posible, de forma relativamente sencilla, predecir la irradiancia solar sobre un plano inclinado a partir de datos de irradiancia sobre plano horizontal. Sucede, además, que dadas las especiales características de la irradiancia solar, los resultados y comportamiento de un sistema no sean fácilmente extrapolables de un lugar a otro por lo que la simulación a través de modelos como los que el presente trabajo aborda son de una importancia relevante.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEpu

A novel clustering based method for characterizing household electricity consumption profiles

A new methodology based on expert knowledge and data mining is proposed to obtain data-driven models that characterize household consumption profiles. These profiles are useful for electricity marketers to understand their customers’ consumption. They could then adjust their electricity purchases in the market and provide recommendations to their customers to manage their consumption. The novelty of this research work is proposing a new procedure to determine an adequate number of clusters for a clustering task. Therefore, the proposed new methodology includes this novel procedure to build the models in two phases. In the first phase, clustering algorithms are used to group the data using different numbers of clusters. For the second phase, a new procedure (k-ISAC_TLP) is proposed and used to select the most appropriate number of clusters. This methodology allows the inclusion of domain information. In the case of household electricity consumption, where only groups with a significant number are relevant as long as the error is small, it allows the use of metrics like the mean absolute error and the number of observations (daily electricity consumption profiles). According to experts, the results achieved in two real datasets (from Spain and Ireland), with millions of observations support the methodology and reveal novel knowledge. In both cases, two and a half million observations have been analyzed and around twenty electricity consumption profiles have been detected. The methodology is easily extensible to problems of any domain where clustering algorithms are applicable. A software solution has been implemented and made freely available.Funding for open access charge: Universidad de Málaga/CBUA . The authors would like to thank the University College Dublin Library the access to the Irish Social Science Data Archive (ISSDA). This work was supported by Grant RTI2018-095097-B-I00 funded by MCIN (Spain), Grant CPP2021-008403 funded by MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”

Promoviendo y Estudiando las Prácticas y Culturas Digitales : el caso del Espacio-Red PCD de Andalucía (UNIA)

Este artículo aborda el proceso de diseño y puesta en funcionamiento del Espacio-Red de Prácticas y Culturas Digitales (PCD), un programa de la Universidad Internacional de Andalucía nacido en 2008 con el objetivo de analizar e impulsar dinámicas sociales y culturales que están emergiendo a partir del uso de los nuevos medios digitales. Se abordan tanto las bases conceptuales como el la ejecución práctica de esta iniciativa.This paper gives an account of the design process and actual implementation of the programme Espacio-Red de Prácticas y Culturas Digitales (PCD), initiated at the Universidad Internacional de Andalucía (UNIA) in 2008 with the aim of analysing and promoting emerging social and cultural dynamics facilitated by the use of new digital media Both the conceptual basis and practical development of this initiative are discusse

Modelos para la predicción del autoconsumo en sistemas fotovoltaicos conectados a red

CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia SolarRESUMEN: En este trabajo se presentan los resultados obtenidos para la modelización y optimización de instalaciones fotovoltaicas de autoconsumo. Se han obtenido las curvas de autoconsumo y autosuficiencia para diferentes perfiles de consumo horario en función de la potencia pico instalada y el tamaño de la batería. El estudio se ha realizado para tres ciudades españolas con diferentes condiciones climáticas. Para la generalización de los resultados se proponen diferentes modelos de aprendizaje automático que permiten estimar estos parámetros. Las variables de entrada de estos modelos están relacionadas con la configuración de la instalación, su ubicación y el tipo de perfil de consumo. El modelo que arroja mejores predicciones en el parámetro de autosuficiencia es Random Forest, que en la validación cruzada tiene un error relativo del 5%. Para la predicción del autoconsumo, el modelo que mejor se comporta es el Perceptrón Multicapa, con un error absoluto promedio de 0.55 y un error relativo del 3%.ABSTRACT: The results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. In order to generalize the obtained results, different models of machine learning are proposed to estimate these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the Multilayer Perceptron, with an average absolute error of 0.55 and a relative error of 3%.info:eu-repo/semantics/publishedVersio

A comprehensive analysis based on GIS-AHP to minimise the social and environmental impact of the installation of large-scale photovoltaic plants in south Spain

This article aims to propose a methodology to assess and prioritise the territorial factors considered when selecting the location of large-scale photovoltaic plants. It therefore seeks to answer the current social conflict problem regarding their installation, particularly in the rural context. The territory zoning is conducted from a holistic perspective, taking into account its social and environmental impact. Therefore, a series of analysis and restriction parameters are established and the Analytic Hierarchy Process by Geographic Information Systems is used. Those criteria are superimposed on the landscape of the Jimena Depression. Located in the south-west of Spain, it is coastal landscape countryside where energy companies currently exert great pressure to expedite applications for photovoltaic projects. A mapping characterization is thus produced that determines which areas should be considered excluded, along with the zones of greater or lesser environmental and social awareness. Assessing this zoning, along with the energy demands and the location of the existing plant projects leads to a series of questions to be considered in the planning of those municipalities. They respond to the need to conduct an overall study, and ones on land protection, the cumulative impact regulation, and on implementing social measures to offset the changes caused.Funding for open access charge: Universidad de Málaga/CBU

Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/µc-Si:H). The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.Peer ReviewedPostprint (author's final draft

Remote supervision and fault detection on OPC monitored PV systems

This paper presents a new approach for automatic supervision and remote fault detection of grid connected photovoltaic (PV) systems by means of OPC technology-based monitoring. The use of standard OPC for monitoring enables data acquisition from a set of devices that use different communication protocols as inverters or other electronic devices present in PV systems enabling universal connectivity and interoperability. Using the OPC standard allows promoting interoperation of software objects in distributed-heterogeneous environments and also allows incorporating in the system remote supervision and diagnosis for the evaluation of grid connected PV facilities. The supervision system analyses the monitored data and evaluates the expected behaviour of main parameters of the PV array: Output voltage, current and power. The monitored data and evaluated parameters are used by the fault detection procedure in order to identify possible faults present in the PV system. The methodology presented has been experimentally validated in the supervision of a grid connected PV system located in Spain. Results obtained show that the combination of OPC monitoring along with the supervision and fault detection procedure is a robust tool that can be very useful in the field of remote supervision and diagnosis of grid connected PV systems. The RMSE between real monitored data and results obtained from the modelling of the PV array were below 3.6% for all parameters even in cloudy days.Peer ReviewedPostprint (published version

Cómo medir las actuaciones urbanas para la descarbonización de las ciudades? Aplicabilidad del índice de sostenibilidad energética urbana en los barrios

CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia SolarRESUMEN: En este trabajo se recoge la experiencia desarrollada con el índice de sostenibilidad energética urbana para las ciudades de Barcelona y Málaga (Márquez-Ballesteros et al, 2019), planteando nuevas vías de trabajo en su aplicabilidad. Se parte de la premisa de que la unidad mínima de actuación, en cuanto a la sostenibilidad energética deberían ser los barrios. La escala urbana de un distrito hace posible las actuaciones globales, desde edificios hasta actuaciones en el espacio público, pasando por aquellas que tienen que ver con la producción local fotovoltaica o la movilidad sostenible, con el máximo acercamiento a los vecinos y vecinas que deberían estar en el centro de toda actuación de mejora. Por lo tanto, las actuaciones en la ciudad barrio a barrio pueden ser una herramienta muy útil en el avance de la sostenibilidad energética. El poder utilizar una herramienta como el índice de sostenibilidad energética urbana para evaluar las actuaciones realizadas en un barrio es un elemento clave para detectar desequilibrios en la ciudad y a su vez acercar la realidad energética a los ciudadanos.ABSTRACT: This work collects all the experience developed with the urban energy sustainability index for the cities of Barcelona and Malaga (Márquez-Ballesteros et al, 2019), proposing new ways of working in its applicability. We start with the premise that the minimum unit of action, in terms of energy sustainability, should be the neighbourhoods. The urban scale of a district makes global measures possible, from buildings to interventions in public space, through those that have to do with local photovoltaic production or sustainable mobility, with the maximum approach to the citizens who should be at the centre of any improvement action. Therefore, interventions in the city neighbourhood by neighbourhood can be a useful tool in the advancement of energy sustainability. The urban energy sustainability index can be used as a tool to evaluate the actions carried out in a neighbourhood and to detect imbalances in the city and finally, bring the energy reality closer to citizens.info:eu-repo/semantics/publishedVersio