Generation and load forecasting for optimization of battery energy management in the context of a nanogrid

Bibliographic Details
Main Author: João Pedro de Bastos Ferreira
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10216/153909
Summary: The dissertation proposal has as its primary objective the development of artificial intelligence algorithms that allow the prediction of photovoltaic production and local load in the context of a nano-grid of a residential area (residence, building, or residential neighborhood). The forecast should estimate very short-term (approx. 3 hrs) and day-ahead (24 hrs) scenarios to enable energy storage to perform peak-shaving, load-shifting, and load-leveling operations.
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spelling Generation and load forecasting for optimization of battery energy management in the context of a nanogridOutras ciências da engenharia e tecnologiasOther engineering and technologiesThe dissertation proposal has as its primary objective the development of artificial intelligence algorithms that allow the prediction of photovoltaic production and local load in the context of a nano-grid of a residential area (residence, building, or residential neighborhood). The forecast should estimate very short-term (approx. 3 hrs) and day-ahead (24 hrs) scenarios to enable energy storage to perform peak-shaving, load-shifting, and load-leveling operations.2023-10-182023-10-18T00:00:00Z2026-10-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/153909TID:203474031engJoão Pedro de Bastos Ferreirainfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-27T17:29:54Zoai:repositorio-aberto.up.pt:10216/153909Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T22:16:21.836671Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
title Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
spellingShingle Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
João Pedro de Bastos Ferreira
Outras ciências da engenharia e tecnologias
Other engineering and technologies
title_short Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
title_full Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
title_fullStr Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
title_full_unstemmed Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
title_sort Generation and load forecasting for optimization of battery energy management in the context of a nanogrid
author João Pedro de Bastos Ferreira
author_facet João Pedro de Bastos Ferreira
author_role author
dc.contributor.author.fl_str_mv João Pedro de Bastos Ferreira
dc.subject.por.fl_str_mv Outras ciências da engenharia e tecnologias
Other engineering and technologies
topic Outras ciências da engenharia e tecnologias
Other engineering and technologies
description The dissertation proposal has as its primary objective the development of artificial intelligence algorithms that allow the prediction of photovoltaic production and local load in the context of a nano-grid of a residential area (residence, building, or residential neighborhood). The forecast should estimate very short-term (approx. 3 hrs) and day-ahead (24 hrs) scenarios to enable energy storage to perform peak-shaving, load-shifting, and load-leveling operations.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-18
2023-10-18T00:00:00Z
2026-10-17T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/153909
TID:203474031
url https://hdl.handle.net/10216/153909
identifier_str_mv TID:203474031
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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