1 resultado para NEURAL-NETWORK ENSEMBLES
em Digital Commons @ DU | University of Denver Research
Filtro por publicador
- Aberdeen University (1)
- Abertay Research Collections - Abertay University’s repository (1)
- Academic Archive On-line (Stockholm University; Sweden) (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 (4)
- Aquatic Commons (3)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (4)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (32)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (8)
- 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 (43)
- Brock University, Canada (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (13)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (36)
- CentAUR: Central Archive University of Reading - UK (83)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (41)
- Cochin University of Science & Technology (CUSAT), India (8)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (177)
- Digital Commons - Michigan Tech (1)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Commons @ Winthrop University (1)
- Digital Commons at Florida International University (8)
- Digital Peer Publishing (5)
- DigitalCommons@The Texas Medical Center (1)
- 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 (1)
- National Center for Biotechnology Information - NCBI (5)
- 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 (54)
- RDBU - Repositório Digital da Biblioteca da Unisinos (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (104)
- 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 (3)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (23)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (5)
- Universidade Federal do Rio Grande do Norte (UFRN) (8)
- Universitat de Girona, Spain (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (4)
- University of Michigan (1)
- University of Queensland eSpace - Australia (22)
- University of Southampton, United Kingdom (1)
- WestminsterResearch - UK (2)
Resumo:
Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.