Using diverse sensors in load forecasting in an office building to support energy management

Autor: Daniel Ramos, Brigida Teixeira, Pedro Faria, Luis Gomes, Omid Abrishambaf, Zita Vale

Publikováno v: Energy Reports, Vol 6, Iss , Pp 182-187 (2020)

The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sus

Externí odkaz: https://doaj.org/article/ed5a30da637244378af29ffc62ec977d

Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Autor: Aria JOZI, Tiago PINTO, Isabel PRAÇA, Francisco SILVA, Brigida TEIXEIRA, Zita VALE

Publikováno v: Advances in Distributed Computing and Artificial Intelligence Journal, Vol 8, Iss 1, Pp 55-64 (2019)

This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three

Externí odkaz: https://doaj.org/article/e2652aa85e654de6ad282e7b0b2b2137

PV Generation Forecasting Model for Energy Management in Buildings

Autor: Tiago Pinto, Brigida Teixeira, Zita Vale, Pedro Faria

Publikováno v: Progress in Artificial Intelligence ISBN: 9783030862299
EPIA

The increasing penetration of renewable energy sources and the need to adjust to the future demand requires adopting measures to improve energy resources management, especially in buildings. In this context, PV generation forecast has an essential ro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3d72e496782ccc6bf8a91ddffe71b51c
https://doi.org/10.1007/978-3-030-86230-5_14

Application Ontology for Multi-Agent and Web-Services’ Co-Simulation in Power and Energy Systems

Autor: Zita Vale, Gabriel Santos, Brigida Teixeira, Juan M. Corchado, Tiago Pinto

Publikováno v: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP

Power and energy systems are very complex, and several tools are available to assist operators in their planning and operation. However, these tools do not allow a sensitive analysis of the impact of the interaction between the different sub-domains

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d448cb380514c2a8a3223b0dd50cc2d
https://hdl.handle.net/10400.22/18442

Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Autor: Tiago Pinto, Francisco Silva, Aria Jozi, Zita Vale, Isabel Praça, Brigida Teixeira

Publikováno v: GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname
Advances in Distributed Computing and Artificial Intelligence Journal, Vol 8, Iss 1, Pp 55-64 (2019)
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP

This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1a38b305746627b5fabeb16b30157ad
http://hdl.handle.net/10366/142772