1 resultado para endogenous losses
em Cochin University of Science
Filtro por publicador
- Repository Napier (1)
- Aberdeen University (1)
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (9)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (9)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (14)
- Aston University Research Archive (17)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (29)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (9)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (62)
- Boston University Digital Common (4)
- Brock University, Canada (2)
- Cambridge University Engineering Department Publications Database (42)
- CentAUR: Central Archive University of Reading - UK (41)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (19)
- Cochin University of Science & Technology (CUSAT), India (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (11)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (5)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (4)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons - Montana Tech (1)
- Digital Commons at Florida International University (3)
- DigitalCommons@The Texas Medical Center (9)
- Duke University (10)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (13)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Greenwich Academic Literature Archive - UK (2)
- Harvard University (2)
- Helda - Digital Repository of University of Helsinki (3)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (25)
- Instituto Politécnico de Viseu (1)
- Instituto Politécnico do Porto, Portugal (4)
- National Center for Biotechnology Information - NCBI (61)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (3)
- Publishing Network for Geoscientific & Environmental Data (3)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (43)
- Queensland University of Technology - ePrints Archive (214)
- Repositorio Academico Digital UANL (1)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (33)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (91)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- School of Medicine, Washington University, United States (1)
- Universidad Autónoma de Nuevo León, Mexico (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (17)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (1)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (3)
- Université de Montréal, Canada (4)
- University of Connecticut - USA (10)
- University of Michigan (32)
- University of Queensland eSpace - Australia (35)
- University of Washington (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.