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The use of computational intelligence techniques for mid-term electricity price forecasting

Bibliographic Details
Main Author: Fonseca, Miguel Fernandes da
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/117659
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
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spelling The use of computational intelligence techniques for mid-term electricity price forecastingEuropean Electricity MarketSupport Vector MachineComputational IntelligenceSDG 7 - Affordable and clean energyDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe currently live in a world ruled by large amounts of data. Organizations’ success is highly determined by the way they foresee and assess changes occurring in the future. Predictive data analytics is the art of building and using models that create forecasts based on patterns extracted from historical data. So, it is a process of making projections about a specific event which the outcome is still unknown in the present. One of the main applications is price prediction (Kelleher, Namee, & D’Arcy, 2015). Price prediction can be applied in innumerous types of business, including the energy sector. Additionally, Big Data has created opportunities for development of new energy services and bears a promise of better energy management and conservation (Grolinger, L’Heureux, Capretz, & Seewald, 2016). Whenever prediction deals with time-series data, it can be designated as forecasting. The electricity spot prices (ESP) represent the result of the market bidding prices outcome, in the electric wholesale market. Predicting these prices is an important and impactful task for market participants, like producers, consumers and retailers, since the principal objective for such players is to achieve the lowest cost in comparison with competitors. ESP play a huge role in energy market’s decision making. It is important both for developing proper bidding strategies as well as for making conscient and sustainable investment decisions (Keynia & Heydari, 2019). Additionally, it impacts the decision of the technologies to use, for example, choosing between renewable energy generators or classic gas turbines. Furthermore, the topic of electricity prices forecasting is extremely relevant for both developed and developing countries. Developed countries search for their economic prospect’s improvement. Electric energy efficiency is a crucial metric for that improvement. Electric energy efficiency can decrease the electricity prices thanks to the reduction of consumption, thus decreasing the need of having new expensive power generation and diminishing the pressure on energy resources. Therefore, ESP behavior is an important factor in their economy. Regarding developing economies, which have faced problems to take the populations out of poverty, the electricity sector restructuring has been fundamental for helping increase the levels of economic development (Ebrahimian, Barmayoon, Mohammadi, & Ghadimi, 2018).Popovič, AlešRUNFonseca, Miguel Fernandes da2021-05-14T15:38:07Z2021-04-072021-04-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/117659TID:202724670enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:53:15Zoai:run.unl.pt:10362/117659Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:24:14.568909Repositórios 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 The use of computational intelligence techniques for mid-term electricity price forecasting
title The use of computational intelligence techniques for mid-term electricity price forecasting
spellingShingle The use of computational intelligence techniques for mid-term electricity price forecasting
Fonseca, Miguel Fernandes da
European Electricity Market
Support Vector Machine
Computational Intelligence
SDG 7 - Affordable and clean energy
title_short The use of computational intelligence techniques for mid-term electricity price forecasting
title_full The use of computational intelligence techniques for mid-term electricity price forecasting
title_fullStr The use of computational intelligence techniques for mid-term electricity price forecasting
title_full_unstemmed The use of computational intelligence techniques for mid-term electricity price forecasting
title_sort The use of computational intelligence techniques for mid-term electricity price forecasting
author Fonseca, Miguel Fernandes da
author_facet Fonseca, Miguel Fernandes da
author_role author
dc.contributor.none.fl_str_mv Popovič, Aleš
RUN
dc.contributor.author.fl_str_mv Fonseca, Miguel Fernandes da
dc.subject.por.fl_str_mv European Electricity Market
Support Vector Machine
Computational Intelligence
SDG 7 - Affordable and clean energy
topic European Electricity Market
Support Vector Machine
Computational Intelligence
SDG 7 - Affordable and clean energy
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
publishDate 2021
dc.date.none.fl_str_mv 2021-05-14T15:38:07Z
2021-04-07
2021-04-07T00:00:00Z
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 http://hdl.handle.net/10362/117659
TID:202724670
url http://hdl.handle.net/10362/117659
identifier_str_mv TID:202724670
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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