Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approach

This research was supported by a UKRI-NERC Constructing a Digital Environment Strategic Priority grant “Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study” [NE/S016236/1 & NE/S016244/1].Peer reviewedPostprin

Sistema de gestão de energia baseado em AI/ML para comunidades de energia renovável

The need for a reorganization of the energy market, with the goal of reducing the energy consumption from non-renewable sources, led to the creation of Renewable Energy Communities, which allow their members to share their produced and stored energy among themselves. The present work proposes a study of a management system of this community, using AI/ML techniques for the energy consumption forecast. It is predicted that, with the use of these techniques, the management system will be able to decrease the electricity bill of the community, or the reduction of energy consumption from the distri-bution grid.A necessidade de reorganização do mercado de energia, com o objetivo de re-duzir o consumo de energia de fontes não renováveis, levou à criação de Co-munidades de Energia Renovável, que permitem que os seus membros parti-lhem a sua energia produzida e armazenada entre si. O presente trabalho pro-põe um estudo sobre um sistema de gestão desta comunidade, usando técnicas de AI/ML para a previsão do consumo de eletricidade. Prevê-se que, com a uti-lização destas técnicas, o sistema de gestão conseguirá diminuir o preço da fatura de eletricidade da comunidade, ou a redução do consumo de energia pro-veniente da rede de distribuição.Mestrado em Engenharia Informátic

Design of an artificial intelligence model that refines the results of analysis in economic decision making. The case of final energy consumption in the UE

[ES] El objetivo de este Trabajo de Fin de Grado, titulado ‘Diseño de un modelo de Inteligencia Artificial que afina los resultados del análisis en la toma de decisiones en economía: el caso del consumo final de energía en la UE’, es arrojar luz sobre el potencial de la Inteligencia Artificial (IA) para el proceso de toma de decisiones. El estudio comienza estableciendo las bases del enfoque tradicional para la toma de decisiones y analizando los procesos que existen. Además, se definen las limitaciones de estos enfoques tradicionales. La siguiente sección explora los nuevos enfoques basados en IA. En primer lugar, se establecen los conceptos fundamentales de la IA. Además, se aclaran las diferencias entre IA y ‘Machine Learning’. Luego, se profundiza en el proceso de toma de decisiones utilizando esta tecnología. Habiendo sentado las bases, la investigación se adentra en los efectos económicos que surgen de la integración de la IA en empresas y organizaciones internacionales, diferenciando entre el nivel de la empresa y las repercusiones micro y macroeconómicas. Por último, se presenta un estudio de un caso real en el sector energético europeo, para comparar el rendimiento predictivo de los métodos tradicionales con las técnicas novedosas de la IA.[EN] The objective of this Final Degree Dissertation, entitled ‘Design of an Artificial Intelligence model that refines the results of the analysis in economic decision making: the case of final energy consumption in the UE’ is to shed light on the potential of Artificial Intelligence (AI) from a decision-maker perspective. The study commences by establishing the bases of the traditional way of conducting data-driven decisions and discussing the various processes that exist. Additionally, it defines the limitations of these traditional approaches. The subsequent section explores the novel AI approaches for the decision making process, first, it establishes the fundamental concepts of AI. Additionally, it clarifies the distinctions between AI and Machine Learning. Then, it dives into the process of making decisions using AI, proposing examples of techniques employed in different areas. Having laid the groundwork, the research delves into the economic effects that arise from the integration of AI in businesses and international organizations, distinguishing between firm level, and microeconomic and macroeconomic level repercussions. Lastly, a real-world case study in the European energy sector is presented, to compare the predictive performance of traditional methods with AI techniques

GPU Accelerated Classifier Benchmarking for Wildfire Related Tasks

Forest fires cause devastating amounts of damage generating negative consequences in the economy, the environment, the populations’ quality of life and in worst case the loss of lives. Having this in mind, the quick and timely prediction of forest fires is a major factor in the mitigation or even negation of the aforementioned consequences. Remote sensing is the process of obtaining information about an object or phenomena without direct interaction. This is the premise on which satellites acquire data of planet Earth. These observations produce enormous amounts of data on a daily basis. This data can be used to find correlation between land surface variables and conditions that are prone to fire ignition. Recently, in this field of study, there has been an effort to automate the process of correlation using machine learning techniques, such as Support Vector Machines and Artificial Neural Networks, in conjunction with a data mining approach, where historical data of a specific area is analysed in order to sort out the major primers of forest fire ignitions and identifying trends. The drawback of this approach is the large amount of time even the simplest task takes to process. GPU processing is the most recent strategy to accelerate this process. The thesis aims to study the behaviour of GPU parallelized classifiers with the ever increasing amounts of data to process and understand if these are appropriate for use in forest predictive tasks

AI-based algorithm for intrusion detection on a real Dataset

[Abstract]: In this Project, Novel Machine Learning proposals are given to produce a Network Intrusion Detection System (NIDS). For this, a state of the art Dataset for Cyclo Stationary NIDS has been used, together with a previously proposed standard methodology to compare the results of different models over the same Dataset. An extensive research has been done for this Project about the different Datasets available for NIDS, as has been done to expose the evolution and functioning of IDSs. Finally, experiments have been made with Outlier Detectors, Ensemble Methods, Deep Learning and Conventional Classifiers to compare with previously published results over the same Dataset and with the same methodology. The findings reveal that the Ensemble Methods have been capable to improve the results from prior research being the best approach the Extreme Gradient Boosting method.[Resumen]: En este Proyecto, se presentan novedosas propuestas de Aprendizaje Automático para producir un Sistema de Detección de Intrusos en Red (NIDS). Para ello, se ha utilizado un Dataset de última generación para NIDS Cicloestacionarios, junto con una metodología estándar previamente propuesta para comparar los resultados de diferentes modelos sobre el mismo Dataset. Para este Proyecto se ha realizado una extensa investigación sobre los diferentes conjuntos de datos disponibles para NIDS, así como se ha expuesto la evolución y funcionamiento de los IDSs. Por último, se han realizado experimentos con Detectores de Anomalias, Métodos de Conjunto, Aprendizaje Profundo y Clasificadores Convencionales para comparar con resultados previamente publicados sobre el mismo Dataset y con la misma metodología. Los resultados revelan que los Métodos de Conjunto han sido capaces de mejorar los resultados de investigaciones previas siendo el mejor enfoque el método de Extreme Gradient Boosting.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

Machine Learning for Understanding and Predicting Injuries in Football

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data