Forecasting electric energy demand using a predictor model based on liquid state machine
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da FURG (RI FURG) |
Texto Completo: | http://repositorio.furg.br/handle/1/4770 |
Resumo: | Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSMs) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing LSMs in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series. |
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Grando, NeusaCenteno, Tania MezzadriBotelho, Silvia Silva da CostaFontoura, Felipe Michels2015-03-06T16:02:07Z2015-03-06T16:02:07Z2010GRANDO, Neusa et al. Forecasting electric energy demand using a predictor model based on liquid state machine. International Journal of Artificial Intelligence and Expert Systems, v. 1, n. 2, 2010. Disponível em: <http://www.cscjournals.org/manuscript/Journals/IJAE/volume1/Issue2/IJAE-14.pdf>. Acesso em: 04 mar. 2015.2180-124Xhttp://repositorio.furg.br/handle/1/4770Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSMs) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing LSMs in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.engLiquid state machinePulsed neural networksPredictionElectric energy demandForecasting electric energy demand using a predictor model based on liquid state machineinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FURG (RI FURG)instname:Universidade Federal do Rio Grande (FURG)instacron:FURGORIGINALIJAE-14.pdfIJAE-14.pdfapplication/pdf329070https://repositorio.furg.br/bitstream/1/4770/1/IJAE-14.pdfbfba6c5bc71b4408a51c6355923b120aMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.furg.br/bitstream/1/4770/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52open access1/47702015-03-06 13:02:07.997open accessoai:repositorio.furg.br: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Repositório InstitucionalPUBhttps://repositorio.furg.br/oai/request || http://200.19.254.174/oai/requestopendoar:2015-03-06T16:02:07Repositório Institucional da FURG (RI FURG) - Universidade Federal do Rio Grande (FURG)false |
dc.title.pt_BR.fl_str_mv |
Forecasting electric energy demand using a predictor model based on liquid state machine |
title |
Forecasting electric energy demand using a predictor model based on liquid state machine |
spellingShingle |
Forecasting electric energy demand using a predictor model based on liquid state machine Grando, Neusa Liquid state machine Pulsed neural networks Prediction Electric energy demand |
title_short |
Forecasting electric energy demand using a predictor model based on liquid state machine |
title_full |
Forecasting electric energy demand using a predictor model based on liquid state machine |
title_fullStr |
Forecasting electric energy demand using a predictor model based on liquid state machine |
title_full_unstemmed |
Forecasting electric energy demand using a predictor model based on liquid state machine |
title_sort |
Forecasting electric energy demand using a predictor model based on liquid state machine |
author |
Grando, Neusa |
author_facet |
Grando, Neusa Centeno, Tania Mezzadri Botelho, Silvia Silva da Costa Fontoura, Felipe Michels |
author_role |
author |
author2 |
Centeno, Tania Mezzadri Botelho, Silvia Silva da Costa Fontoura, Felipe Michels |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Grando, Neusa Centeno, Tania Mezzadri Botelho, Silvia Silva da Costa Fontoura, Felipe Michels |
dc.subject.por.fl_str_mv |
Liquid state machine Pulsed neural networks Prediction Electric energy demand |
topic |
Liquid state machine Pulsed neural networks Prediction Electric energy demand |
description |
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSMs) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing LSMs in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series. |
publishDate |
2010 |
dc.date.issued.fl_str_mv |
2010 |
dc.date.accessioned.fl_str_mv |
2015-03-06T16:02:07Z |
dc.date.available.fl_str_mv |
2015-03-06T16:02:07Z |
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.citation.fl_str_mv |
GRANDO, Neusa et al. Forecasting electric energy demand using a predictor model based on liquid state machine. International Journal of Artificial Intelligence and Expert Systems, v. 1, n. 2, 2010. Disponível em: <http://www.cscjournals.org/manuscript/Journals/IJAE/volume1/Issue2/IJAE-14.pdf>. Acesso em: 04 mar. 2015. |
dc.identifier.uri.fl_str_mv |
http://repositorio.furg.br/handle/1/4770 |
dc.identifier.issn.none.fl_str_mv |
2180-124X |
identifier_str_mv |
GRANDO, Neusa et al. Forecasting electric energy demand using a predictor model based on liquid state machine. International Journal of Artificial Intelligence and Expert Systems, v. 1, n. 2, 2010. Disponível em: <http://www.cscjournals.org/manuscript/Journals/IJAE/volume1/Issue2/IJAE-14.pdf>. Acesso em: 04 mar. 2015. 2180-124X |
url |
http://repositorio.furg.br/handle/1/4770 |
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 |
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reponame:Repositório Institucional da FURG (RI FURG) instname:Universidade Federal do Rio Grande (FURG) instacron:FURG |
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Universidade Federal do Rio Grande (FURG) |
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FURG |
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FURG |
reponame_str |
Repositório Institucional da FURG (RI FURG) |
collection |
Repositório Institucional da FURG (RI FURG) |
bitstream.url.fl_str_mv |
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