Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/10029 |
Resumo: | Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel’s Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources. |
id |
RCAP_7f6d7dfe76f137a9d7adefe0efa8770c |
---|---|
oai_identifier_str |
oai:recipp.ipp.pt:10400.22/10029 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental TemperatureElectricity ConsumptionEnvironmental TemperatureForecastingWang and Mendel’s Fuzzy RuleFuzzy Rule Based SystemsReliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel’s Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.Institute of Electrical and Electronics EngineersRepositório Científico do Instituto Politécnico do PortoJozi, AriaPinto, TiagoPraça, IsabelSilva, FranciscoTeixeira, BrigidaVale, Zita20162117-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/10029eng10.1109/GIIS.2016.7814944metadata only accessinfo: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:RCAAP2023-03-13T12:51:34Zoai:recipp.ipp.pt:10400.22/10029Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:30:32.837638Repositório 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 |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
title |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
spellingShingle |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature Jozi, Aria Electricity Consumption Environmental Temperature Forecasting Wang and Mendel’s Fuzzy Rule Fuzzy Rule Based Systems |
title_short |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
title_full |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
title_fullStr |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
title_full_unstemmed |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
title_sort |
Wang and Mendel’s Fuzzy Rule Learning Method for Energy Consumption Forecasting considering the Influence of Environmental Temperature |
author |
Jozi, Aria |
author_facet |
Jozi, Aria Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brigida Vale, Zita |
author_role |
author |
author2 |
Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brigida Vale, Zita |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Jozi, Aria Pinto, Tiago Praça, Isabel Silva, Francisco Teixeira, Brigida Vale, Zita |
dc.subject.por.fl_str_mv |
Electricity Consumption Environmental Temperature Forecasting Wang and Mendel’s Fuzzy Rule Fuzzy Rule Based Systems |
topic |
Electricity Consumption Environmental Temperature Forecasting Wang and Mendel’s Fuzzy Rule Fuzzy Rule Based Systems |
description |
Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel’s Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z 2117-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/10029 |
url |
http://hdl.handle.net/10400.22/10029 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/GIIS.2016.7814944 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799131400775925760 |