114 research outputs found

    Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments

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    Applying Machine Learning (ML) manually to a given problem setting is a tedious and time-consuming process which brings many challenges with it, especially in the context of Big Data. In such a context, gaining insightful information, finding patterns, and extracting knowledge from large datasets are quite complex tasks. Additionally, the configurations of the underlying Big Data infrastructure introduce more complexity for configuring and running ML tasks. With the growing interest in ML the last few years, particularly people without extensive ML expertise have a high demand for frameworks assisting people in applying the right ML algorithm to their problem setting. This is especially true in the field of smart energy system applications where more and more ML algorithms are used e.g. for time series forecasting. Generally, two groups of non-expert users are distinguished to perform energy time series forecasting. The first one includes the users who are familiar with statistics and ML but are not able to write the necessary programming code for training and evaluating ML models using the well-known trial-and-error approach. Such an approach is time consuming and wastes resources for constructing multiple models. The second group is even more inexperienced in programming and not knowledgeable in statistics and ML but wants to apply given ML solutions to their problem settings. The goal of this thesis is to scientifically explore, in the context of more concrete use cases in the energy domain, how such non-expert users can be optimally supported in creating and performing ML tasks in practice on cluster computing environments. To support the first group of non-expert users, an easy-to-use modular extendable microservice-based ML solution for instrumenting and evaluating ML algorithms on top of a Big Data technology stack is conceptualized and evaluated. Our proposed solution facilitates applying trial-and-error approach by hiding the low level complexities from the users and introduces the best conditions to efficiently perform ML tasks in cluster computing environments. To support the second group of non-expert users, the first solution is extended to realize meta learning approaches for automated model selection. We evaluate how meta learning technology can be efficiently applied to the problem space of data analytics for smart energy systems to assist energy system experts which are not data analytics experts in applying the right ML algorithms to their data analytics problems. To enhance the predictive performance of meta learning, an efficient characterization of energy time series datasets is required. To this end, Descriptive Statistics Time based Meta Features (DSTMF), a new kind of meta features, is designed to accurately capture the deep characteristics of energy time series datasets. We find that DSTMF outperforms the other state-of-the-art meta feature sets introduced in the literature to characterize energy time series datasets in terms of the accuracy of meta learning models and the time needed to extract them. Further enhancement in the predictive performance of the meta learning classification model is achieved by training the meta learner on new efficient meta examples. To this end, we proposed two new approaches to generate new energy time series datasets to be used as training meta examples by the meta learner depending on the type of time series dataset (i.e. generation or energy consumption time series). We find that extending the original training sets with new meta examples generated by our approaches outperformed the case in which the original is extended by new simulated energy time series datasets

    Cost effective technology applied to domotics and smart home energy management systems

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    Premio extraordinario de Trabajo Fin de Máster curso 2019/2020. Máster en Energías Renovables DistribuidasIn this document is presented the state of art for domotics cost effective technologies available on market nowadays, and how to apply them in Smart Home Energy Management Systems (SHEMS) allowing peaks shaving, renewable management and home appliance controls, always in cost effective context in order to be massively applied. Additionally, beyond of SHEMS context, it will be also analysed how to apply this technology in order to increase homes energy efficiency and monitoring of home appliances. Energy management is one of the milestones for distributed renewable energy spread; since renewable energy sources are not time-schedulable, are required control systems capable of the management for exchanging energy between conventional sources (power grid), renewable sources and energy storage sources. With the proposed approach, there is a first block dedicated to show an overview of Smart Home Energy Management Systems (SMHEMS) classical architecture and functional modules of SHEMS; next step is to analyse principles which has allowed some devices to become a cost-effective technology. Once the technology has been analysed, it will be reviewed some specific resources (hardware and software) available on marked for allowing low cost SHEMS. Knowing the “tools” available; it will be shown how to adapt classical SHEMS to cost effective technology. Such way, this document will show some specific applications of SHEMS. Firstly, in a general point of view, comparing the proposed low-cost technology with one of the main existing commercial proposals; and secondly, developing the solution for a specific real case.En este documento se aborda el estado actual de la domótica de bajo coste disponible en el mercado actualmente y cómo aplicarlo en los sistemas inteligentes de gestión energética en la vivienda (SHEMS) permitiendo el recorte de las puntas de demanda, gestión de energías renovables y control de electrodomésticos, siempre en el contexto del bajo coste, con el objetivo de lograr la máxima difusión de los SHEMS. Adicionalmente, más allá del contexto de la tecnología SHEMS, se analizará cómo aplicar esta tecnología para aumentar la eficiencia energética de los hogares y para la supervisión de los electrodomésticos. La gestión energética es uno de los factores principales para lograr la difusión de las energías renovables distribuidas; debido a que las fuentes de energía renovable no pueden ser planificadas, se requieren sistemas de control capaces de gestionar el intercambio de energía entre las fuentes convencionales (red eléctrica de distribución), energías renovables y dispositivos de almacenamiento energético. Bajo esta perspectiva, este documento presenta un primer bloque en el que se exponen las bases de la arquitectura y módulos funcionales de los sistemas inteligentes de gestión energética en la vivienda (SHEMS); el siguiente paso será analizar los principios que han permitido a ciertos dispositivos convertirse en dispositivos de bajo coste. Una vez analizada la tecnología, nos centraremos en los recursos (hardware y software) existentes que permitirán la realización de un SHEMS a bajo coste. Conocidas las “herramientas” a nuestra disposición, se mostrará como adaptar un esquema SHEMS clásico a la tecnología de bajo coste. Primeramente, comparando de modo genérico la tecnología de bajo coste con una de las principales propuestas comerciales de SHEMS, para seguidamente desarrollar la solución de bajo coste a un caso específico real

    FIFTY YEARS OF MICROPROCESSOR EVOLUTION: FROM SINGLE CPU TO MULTICORE AND MANYCORE SYSTEMS

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    Nowadays microprocessors are among the most complex electronic systems that man has ever designed. One small silicon chip can contain the complete processor, large memory and logic needed to connect it to the input-output devices. The performance of today's processors implemented on a single chip surpasses the performance of a room-sized supercomputer from just 50 years ago, which cost over $ 10 million [1]. Even the embedded processors found in everyday devices such as mobile phones are far more powerful than computer developers once imagined. The main components of a modern microprocessor are a number of general-purpose cores, a graphics processing unit, a shared cache, memory and input-output interface and a network on a chip to interconnect all these components [2]. The speed of the microprocessor is determined by its clock frequency and cannot exceed a certain limit. Namely, as the frequency increases, the power dissipation increases too, and consequently the amount of heating becomes critical. So, silicon manufacturers decided to design new processor architecture, called multicore processors [3]. With aim to increase performance and efficiency these multiple cores execute multiple instructions simultaneously. In this way, the amount of parallel computing or parallelism is increased [4]. In spite of mentioned advantages, numerous challenges must be addressed carefully when more cores and parallelism are used.This paper presents a review of microprocessor microarchitectures, discussing their generations over the past 50 years. Then, it describes the currently used implementations of the microarchitecture of modern microprocessors, pointing out the specifics of parallel computing in heterogeneous microprocessor systems. To use efficiently the possibility of multi-core technology, software applications must be multithreaded. The program execution must be distributed among the multi-core processors so they can operate simultaneously. To use multi-threading, it is imperative for programmer to understand the basic principles of parallel computing and parallel hardware. Finally, the paper provides details how to implement hardware parallelism in multicore systems

    Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

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    This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

    Situation Awareness for Smart Distribution Systems

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    In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

    Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

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    Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

    Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

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    The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

    Energy Efficiency

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    This book is one of the most comprehensive and up-to-date books written on Energy Efficiency. The readers will learn about different technologies for energy efficiency policies and programs to reduce the amount of energy. The book provides some studies and specific sets of policies and programs that are implemented in order to maximize the potential for energy efficiency improvement. It contains unique insights from scientists with academic and industrial expertise in the field of energy efficiency collected in this multi-disciplinary forum
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