Forecasting hourly prices in the portuguese power market with ARIMA models

Detalhes bibliográficos
Autor(a) principal: Dias, António Vasconcellos
Data de Publicação: 2009
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/2040
Resumo: As power markets became a recent worldwide phenomenon, electricity prices’ forecast is a new subject for investigators. Due to the electricity’s particularities, a power market has some very specific rules that must be understood before one begins its study. This empirical research presents a comparative study between two forecasting methods of the day-ahead hourly electricity prices in the Portuguese power market: a complete hourly time-series analysis and an hour-by-hour approach, each one for a Summer and an Autumn seasons. My purpose is to check if an exhaustive hourly analysis would improve significantly the energy price forecasts accuracy and, if so, would the additional computing time offsets this improvement. As it is common in energy prices empirical research, we use ARIMA models. To select the models on a first stage, the Mincer- Zarnowitz regression was considered. On a second stage, to compare the models and select the best one in terms of predictive ability, the Harvey-Newbold encompassing test was applied. Some evidence was found that, in accordance to Cuaresma et al. (2004), analysing each hour separately produced better results than considering the complete time series, although the time taken to estimate the models can be an issue for short term predictions. The ARIMA models that captured the weekly effect encompassed the others and produced more accurate forecasts.
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spelling Forecasting hourly prices in the portuguese power market with ARIMA modelsElectricity MarketTime-series analysisEnergy pricePrice forecastMercado eléctricoAnálise de séries temporaisPreços de energiaPrevisão de preçosAs power markets became a recent worldwide phenomenon, electricity prices’ forecast is a new subject for investigators. Due to the electricity’s particularities, a power market has some very specific rules that must be understood before one begins its study. This empirical research presents a comparative study between two forecasting methods of the day-ahead hourly electricity prices in the Portuguese power market: a complete hourly time-series analysis and an hour-by-hour approach, each one for a Summer and an Autumn seasons. My purpose is to check if an exhaustive hourly analysis would improve significantly the energy price forecasts accuracy and, if so, would the additional computing time offsets this improvement. As it is common in energy prices empirical research, we use ARIMA models. To select the models on a first stage, the Mincer- Zarnowitz regression was considered. On a second stage, to compare the models and select the best one in terms of predictive ability, the Harvey-Newbold encompassing test was applied. Some evidence was found that, in accordance to Cuaresma et al. (2004), analysing each hour separately produced better results than considering the complete time series, although the time taken to estimate the models can be an issue for short term predictions. The ARIMA models that captured the weekly effect encompassed the others and produced more accurate forecasts.Com a transformação dos mercados de electricidade num fenómeno mundial, a previsão de preços de electricidade tornou-se num novo tema de estudo para os investigadores. Devido às particularidades da electricidade, um mercado eléctrico tem regras muito específicas que têm que ser compreendidas antes de se iniciar o seu estudo. Este trabalho experimental apresenta um estudo comparativo entre dois métodos de previsão dos preços horários de electricidade para o dia seguinte: uma análise da série horária completa e uma aproximação hora a hora, cada uma delas para um período de Verão e de Outono. O meu objectivo é verificar se uma análise horária exaustiva melhora significativamente a precisão das previsões dos preços de energia e, caso tal se verifique, se o tempo adicional requerido compensa esta melhoria. Como tem sido comum em estudos empíricos sobre preços de energia, utilizámos modelos ARIMA. Para seleccionar os modelos foi considerada a regressão de Mincer-Zarnowitz numa primeira fase. Num segundo momento, para comparar os modelos e seleccionar o melhor no que respeita à capacidade preditiva, o teste de Harvey-Newbold foi aplicado. Encontrámos evidências de que, de acordo com Cuaresma et al. (2004), analisar cada hora separadamente conduz a melhores resultados do que considerar a série temporal completa, embora o tempo requerido para estimar os modelos seja relevante para previsões de curto-prazo. Os modelos ARIMA que captaram o efeito semanal englobavam os outros e produziram previsões mais precisas.2010-08-26T10:49:38Z2010-08-26T00:00:00Z2010-08-262009info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/octet-streamhttp://hdl.handle.net/10071/2040engDias, António Vasconcellosinfo: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:RCAAP2023-11-09T17:41:35Zoai:repositorio.iscte-iul.pt:10071/2040Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:19:21.693782Repositório 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 Forecasting hourly prices in the portuguese power market with ARIMA models
title Forecasting hourly prices in the portuguese power market with ARIMA models
spellingShingle Forecasting hourly prices in the portuguese power market with ARIMA models
Dias, António Vasconcellos
Electricity Market
Time-series analysis
Energy price
Price forecast
Mercado eléctrico
Análise de séries temporais
Preços de energia
Previsão de preços
title_short Forecasting hourly prices in the portuguese power market with ARIMA models
title_full Forecasting hourly prices in the portuguese power market with ARIMA models
title_fullStr Forecasting hourly prices in the portuguese power market with ARIMA models
title_full_unstemmed Forecasting hourly prices in the portuguese power market with ARIMA models
title_sort Forecasting hourly prices in the portuguese power market with ARIMA models
author Dias, António Vasconcellos
author_facet Dias, António Vasconcellos
author_role author
dc.contributor.author.fl_str_mv Dias, António Vasconcellos
dc.subject.por.fl_str_mv Electricity Market
Time-series analysis
Energy price
Price forecast
Mercado eléctrico
Análise de séries temporais
Preços de energia
Previsão de preços
topic Electricity Market
Time-series analysis
Energy price
Price forecast
Mercado eléctrico
Análise de séries temporais
Preços de energia
Previsão de preços
description As power markets became a recent worldwide phenomenon, electricity prices’ forecast is a new subject for investigators. Due to the electricity’s particularities, a power market has some very specific rules that must be understood before one begins its study. This empirical research presents a comparative study between two forecasting methods of the day-ahead hourly electricity prices in the Portuguese power market: a complete hourly time-series analysis and an hour-by-hour approach, each one for a Summer and an Autumn seasons. My purpose is to check if an exhaustive hourly analysis would improve significantly the energy price forecasts accuracy and, if so, would the additional computing time offsets this improvement. As it is common in energy prices empirical research, we use ARIMA models. To select the models on a first stage, the Mincer- Zarnowitz regression was considered. On a second stage, to compare the models and select the best one in terms of predictive ability, the Harvey-Newbold encompassing test was applied. Some evidence was found that, in accordance to Cuaresma et al. (2004), analysing each hour separately produced better results than considering the complete time series, although the time taken to estimate the models can be an issue for short term predictions. The ARIMA models that captured the weekly effect encompassed the others and produced more accurate forecasts.
publishDate 2009
dc.date.none.fl_str_mv 2009
2010-08-26T10:49:38Z
2010-08-26T00:00:00Z
2010-08-26
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/10071/2040
url http://hdl.handle.net/10071/2040
dc.language.iso.fl_str_mv eng
language eng
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application/octet-stream
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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)
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