Data Mining Models for Short Term Solar Radiation Prediction and Forecast-Based Assessment of Photovoltaic Facilities

Solar radiation prediction is useful to integrate photovoltaic power plants into the electrical system. Integrating energy generation in urban environments is interesting because that is where the most energy is consumed and avoids wasting energy in transport infrastructure. Renewable energies are often the easiest to integrate into these environments because they require less infrastructure and cause fewer problems related to noise, dirt, pollution, etc. The overall objective of this thesis is to develop data mining models to forecast solar global radiation 24 hours ahead and to use these predictions to evaluate the performance of photovoltaic systems. The specific objectives are: 1. Propose an index that allows us to remove the seasonal and daily trends observed in global hourly radiation data. 2. Analyze the different sources of meteorological variables that can be used to predict solar radiation and use API's to access external sources of meteorological data. 3. Develop data mining models that allow including the different relationships observed between the radiation values of the next day depending on the values of the current day radiation and other meteorological parameters. 4. Development of a web system that include the proposed models for short-term radiation forescasting and integrate the developed models in the evaluation models of photovoltaic systems. Chapter 3 introduces the methods and models used in this work (Cumulative Probability Distribution Function, Artificial Neural Networks and Support Vector Machines). Also classification methods are presented (Decision Trees and Support Vector Machines for Classification). Performance metrics are presented to measure the accuracy of the proposed models. The data sets and data sources used in this work to test the proposed models are presented, including data from the meteorological station installed at University of Malaga, data from OpenWeatherMap website and data from AEMET (Agencia Estatal de Meteorología). Chapter 4 is dedicated to the solar radiation fundamentals, including astronomical concepts related to Earth-Sun position, characterization of solar radiation hourly series, clearnes index, used to remove seasonal trends, persistence model, used to compare with proposed models and the forecast skill, based on persistence model and used as reference model as well. Chapter 5 introduces a model to model and characterize hourly solar global radiation using statistical methods like CPDF, K-means, and also using the clearness index. This models aims to predict the hourly solar radiation using the daily clearness index as input. Chapter 6 details the proposed model to forecast the hourly global solar radiation using data mining methods and daily profiles of clearness index. K-means is again used to cluster daily solar radiation profiles, then a new variable is defined from the clearness index daily profiles. Support Vector Machines, Decision Trees and Artificial Neural Networks are used to predict the desired hourly solar radiation values. Chapter 7 presents a methodology to assess solar power plants performance based on forecasted solar radiation. A OPC-based system is presented, which is able to obtain data from a large variety of equipment, then an algorithm to assess the performance of the plants is presented

Reader comments agentive power in COVID-19 digital news articles : challenging parascientific information?

The recent COVID-19 pandemic has triggered an enormous stream of information. Parascientific digital communication has pursued different avenues, from mainstream media news to social networking, at times combined. Likewise, citizens have developed new discourse practices, with readers as active participants who claim authority. Based on a corpus of 500 reader comments from The Guardian, we analyse how readers build their authorial voice on COVID-19 news as well as their agentive power and its implications. Methodologically, we draw upon stance markers, depersonalisation strategies, and heteroglossic markers, from the perspective of discursive interpersonality. Our findings unearth that stance markers are central for readers to build authority and produce content. Depersonalised and heteroglossic markers are also resorted, reinforcing readers' authority with external information that mirrors expert scientific communication. Conclusions suggest a strong citizen agentive power that can either support news articles, spreading parascientific information, or challenge them, therefore, contributing to produce pseudoscientific messages

Live Demonstration: Real-time neuro-inspired sound source localization and tracking architecture applied to a robotic platform

This live demonstration presents a sound source localization and tracking system implemented with Spike Signal Processing (SSP) building blocks on FPGA devices. The system architecture is based on the ability of the mammalian auditory system to locate the direction of a sound in the horizontal plane using the interaural intensity difference. We used a binaural Neuromorphic Auditory Sensor to obtain spike rates similar to those generated by the inner hair cells of the human auditory system and the component that obtains the interaural intensity difference is inspired by the lateral superior olive. The spike stream that represents the interaural intensity difference is used to turn a robotic platform towards the sound source direction. The system was tested with pure tones (1-kHz, 2.5-kHz and 5- kHz sounds) with an average error of 2.32 degrees.Ministerio de Economía y Competitividad TEC2016-77785-

FPGA Implementations Comparison of Neuro-cortical Inspired Convolution Processors for Spiking Systems

Image convolution operations in digital computer systems are usually very expensive operations in terms of resource consumption (processor resources and processing time) for an efficient Real-Time application. In these scenarios the visual information is divided in frames and each one has to be completely processed before the next frame arrives. Recently a new method for computing convolutions based on the neuro-inspired philosophy of spiking systems (Address-Event-Representation systems, AER) is achieving high performances. In this paper we present two FPGA implementations of AERbased convolution processors that are able to work with 64x64 images and programmable kernels of up to 11x11 elements. The main difference is the use of RAM for integrators in one solution and the absence of integrators in the second solution that is based on mapping operations. The maximum equivalent operation rate is 163.51 MOPS for 11x11 kernels, in a Xilinx Spartan 3 400 FPGA with a 50MHz clock. Formulations, hardware architecture, operation examples and performance comparison with frame-based convolution processors are presented and discussed.Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Junta de Andalucía P06-TIC-0141

On the AER Convolution Processors for FPGA

Image convolution operations in digital computer systems are usually very expensive operations in terms of resource consumption (processor resources and processing time) for an efficient Real-Time application. In these scenarios the visual information is divided into frames and each one has to be completely processed before the next frame arrives in order to warranty the real-time. A spike-based philosophy for computing convolutions based on the neuro-inspired Address-Event- Representation (AER) is achieving high performances. In this paper we present two FPGA implementations of AER-based convolution processors for relatively small Xilinx FPGAs (Spartan-II 200 and Spartan-3 400), which process 64x64 images with 11x11 convolution kernels. The maximum equivalent operation rate that can be reached is 163.51 MOPS for 11x11 kernels, in a Xilinx Spartan 3 400 FPGA with a 50MHz clock. Formulations, hardware architecture, operation examples and performance comparison with frame-based convolution processors are presented and discussed.Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-0141

AER-based robotic closed-loop control system

Address-Event-Representation (AER) is an asynchronous protocol for transferring the information of spiking neuro-inspired systems. Actually AER systems are able to see, to ear, to process information, and to learn. Regarding to the actuation step, the AER has been used for implementing Central Pattern Generator algorithms, but not for controlling the actuators in a closed-loop spike-based way. In this paper we analyze an AER based model for a real-time neuro-inspired closed-loop control system. We demonstrate it into a differential control system for a two-wheel vehicle using feedback AER information. PFM modulation has been used to power the DC motors of the vehicle and translation into AER of encoder information is also presented for the close-loop. A codesign platform (called AER-Robot), based into a Xilinx Spartan 3 FPGA and an 8051 USB microcontroller, with power stages for four DC motors has been used for the demonstrator.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0