57 research outputs found

    Short-term forecasting of wind energy: A comparison of deep learning frameworks

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    Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented

    Attitude of Undergraduate students towards Sketchnoting activity in classroom

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    Ensuring and further increasing student engagement in the classroom is a challenging task for the teachers. Teachers need to be creative with the tasks and activities they employ in the classroom so that students pay more attention to the topic being discussed. Greater the involvement of the students, better would be the chances that they learn. This study focused on studying the attitude of students towards sketchnoting activity in the classroom. College going students from Bachelor of Education (B.Ed.)., Bachelor of Arts (B.A., Applied Psychology) and Bachelor of Science (B.Sc., Clinical Psychology) courses were included in the sample

    Prolegomena to an operator theory of computation

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    Defining computation as information processing (information dynamics) with information as a relational property of data structures (the difference in one system that makes a difference in another system) makes it very suitable to use operator formulation, with similarities to category theory. The concept of the operator is exceedingly important in many knowledge areas as a tool of theoretical studies and practical applications. Here we introduce the operator theory of computing, opening new opportunities for the exploration of computing devices, processes, and their networks

    A Review of Current Research Trends in Power-Electronic Innovations in Cyber-Physical Systems.

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    In this paper, a broad overview of the current research trends in power-electronic innovations in cyber-physical systems (CPSs) is presented. The recent advances in semiconductor device technologies, control architectures, and communication methodologies have enabled researchers to develop integrated smart CPSs that can cater to the emerging requirements of smart grids, renewable energy, electric vehicles, trains, ships, internet of things (IoTs), etc. The topics presented in this paper include novel power-distribution architectures, protection techniques considering large renewable integration in smart grids, wireless charging in electric vehicles, simultaneous power and information transmission, multi-hop network-based coordination, power technologies for renewable energy and smart transformer, CPS reliability, transactive smart railway grid, and real-time simulation of shipboard power systems. It is anticipated that the research trends presented in this paper will provide a timely and useful overview to the power-electronics researchers with broad applications in CPSs.post-print2.019 K

    Hydrolink 2018/2. Leisure Hydraulics

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    Topic: Leisure Hydraulic

    System organization and operation in the context of local flexibility markets at distribution level

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    9. Industry, innovation and infrastructur

    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
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