Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum

Detalhes bibliográficos
Autor(a) principal: Agottani, Luis Fernando Rodrigues
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149105
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Short-term forecasting for household electricity load with dynamic feature selection using power cepstrumShort-term energy load ForecastingDynamic selectionPower CepstrumMachine LearningConvLSTMDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceElectrical energy is present in our civilization and has positive and negative impacts in our environment, renewable and green energy like solar and wind energy works with significant less negative environmental impacts, reducing disasters and fuel dependency. Although, the transition to renewable and green energy demands advanced technologies to manage energy distribution in society, since the clean sources are stochastic. In this study the research will be done to improve electricity consumption forecasting in a household, a tool that can help the energy distribution management by microgrids to determine the amount of energy used by consumers at a particular moment, resulting in reducing energy waste and allowing P2P energy trading. The goal of this study is to do short-term forecasting and test the ability of Power Cepstrum to select autoregressive features, the dataset used is of minute-by-minute electricity consumption in kilowatts of a single household in the town of Sceaux,, France, between December 2006 and November 2010, the model tested was the Convolutional Long Short-Term Memory neural network with selected auto-regressive feature model (CLSAF), a Convolutional Long Short-Term Memory model working with Persistence model with dynamic feature selection, the ability of Power Cepstrum to select the autoregressive feature is tested and compared to CLSAF using Autocorrelation Function to select the autoregressive feature, the results are compared either to state of art models such as ConvLSTM and Persistence model. The tests were done comparing different theta threshold, input lags, resolutions, and input length. The result show that Power Cepstrum can be used as a replacement for Autocorrelation Function, CLSAF have comparable accuracy to ConvLSTM model and better runtime performance when using y[t-1] as input lag, for 30 minutes resolution is possible to observe great difference between runtime prediction without losing accuracy performance, Power Cepstrum showed better runtime prediction when compared to autocorrelation function, also, higher input length improved models performance.Scott, Ian JamesRUNAgottani, Luis Fernando Rodrigues2023-02-13T13:24:21Z2023-01-242023-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149105TID:203221338enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:30:51Zoai:run.unl.pt:10362/149105Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:36.822165Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
title Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
spellingShingle Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
Agottani, Luis Fernando Rodrigues
Short-term energy load Forecasting
Dynamic selection
Power Cepstrum
Machine Learning
ConvLSTM
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
title_full Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
title_fullStr Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
title_full_unstemmed Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
title_sort Short-term forecasting for household electricity load with dynamic feature selection using power cepstrum
author Agottani, Luis Fernando Rodrigues
author_facet Agottani, Luis Fernando Rodrigues
author_role author
dc.contributor.none.fl_str_mv Scott, Ian James
RUN
dc.contributor.author.fl_str_mv Agottani, Luis Fernando Rodrigues
dc.subject.por.fl_str_mv Short-term energy load Forecasting
Dynamic selection
Power Cepstrum
Machine Learning
ConvLSTM
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Short-term energy load Forecasting
Dynamic selection
Power Cepstrum
Machine Learning
ConvLSTM
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-02-13T13:24:21Z
2023-01-24
2023-01-24T00:00:00Z
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/10362/149105
TID:203221338
url http://hdl.handle.net/10362/149105
identifier_str_mv TID:203221338
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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