Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.This contribution has been supported by the Cátedra ELAND for Renewable Energies of the University of Jaén, by the Spanish government by means of the project RTI2018-098979-A-I00. This work has been partially funded by “La Conselleria de Innovacién, Universidades, Ciencia y Sociedad Digital”, under the project “Development of an architecture based on machine learning and data mining techniques for the prediction of indicators in the diagnosis and intervention of autism spectrum disorder. AICO/2020/117”

On-site measurement of limiting subcell in multijunction solar devices

It is well known that the response of any photovoltaic solar cell is dependent on the spectral characteristics of the incident radiation. This dependency is crucial in the output characteristics of a multijunction (MJ) cell where the spectral composition of the radiation determines the overall photocurrent produced, as either the top or the middle subcell will be limiting its response. The current mismatching between top and middle subcell is translated into energy losses, affecting the yield of the system. For research and commercial purposes it is interesting to measure accurately the incident solar radiation on a MJ cell, in terms of its spectral composition. This measurement will allows us to determine the photocurrent generated in each band of the multijunction device. Nowadays, the only way of measuring the photocurrent generated by each subcell is done with isotype cells or with spectroradiometers but there is no device capable of directly measuring each subcell photocurrent. In this paper it is described a device based on a commercial multijunction solar cell that is capable of measuring the direct irradiance for the top and middle bands thus it offers information of the limiting subcell (top or middle) in outdoors conditions

Divulgación de proyectos de energía solar fotovoltaica de la provincia de Jaén en Internet

El trabajo presentamos ha consistido en la realización de una Web para la red Internet, con el fin de dar a conocer algunos de los diferentes proyectos en el área de la energía solar fotovoltaica que se están realizando en la provincia de Jaén, fruto del esfuerzo de participación de diferentes organismos: Empresas del ramo, Ayuntamiento, Diputación Provincial, Universidad, Instituto de Estudios Giennenses. Web es el nombre que se ha dado a un gran conjunto de información a la que se puede acceder a través de Internet. Esta información se encuentra organizada en páginas web, y cada página puede contener, texto, imágenes, sonido, vídeo, además de otros elementos [1]

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance