1 resultado para Recurrent neural network
em Repositório Científico do Instituto Politécnico de Lisboa - Portugal
Filtro por publicador
- Aberdeen University (1)
- Abertay Research Collections - Abertay University’s repository (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Aquatic Commons (3)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (3)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (3)
- Aston University Research Archive (34)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (4)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (6)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (20)
- Boston University Digital Common (44)
- Brock University, Canada (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (15)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (37)
- CentAUR: Central Archive University of Reading - UK (89)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (41)
- Cochin University of Science & Technology (CUSAT), India (11)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Dalarna University College Electronic Archive (2)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Commons @ Winthrop University (1)
- Digital Commons at Florida International University (7)
- Digital Peer Publishing (3)
- Duke University (3)
- Greenwich Academic Literature Archive - UK (5)
- Helda - Digital Repository of University of Helsinki (4)
- Indian Institute of Science - Bangalore - Índia (50)
- Instituto de Engenharia Nuclear, Brazil - Carpe dIEN (2)
- Instituto Politécnico de Bragança (1)
- Instituto Politécnico do Porto, Portugal (7)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (2)
- Massachusetts Institute of Technology (3)
- National Center for Biotechnology Information - NCBI (1)
- Nottingham eTheses (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (5)
- Publishing Network for Geoscientific & Environmental Data (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (92)
- Queensland University of Technology - ePrints Archive (56)
- RDBU - Repositório Digital da Biblioteca da Unisinos (3)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (97)
- Research Open Access Repository of the University of East London. (5)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (2)
- SAPIENTIA - Universidade do Algarve - Portugal (15)
- SerWisS - Server für Wissenschaftliche Schriften der Fachhochschule Hannover (1)
- Universidad Autónoma de Nuevo León, Mexico (1)
- Universidad de Alicante (2)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (8)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (6)
- Universidade Federal do Rio Grande do Norte (UFRN) (10)
- Universitat de Girona, Spain (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal (1)
- Université de Montréal, Canada (6)
- University of Michigan (1)
- University of Queensland eSpace - Australia (26)
- University of Southampton, United Kingdom (1)
- WestminsterResearch - UK (2)
Resumo:
This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.