Aprimoramento da Predição de Carga Elétrica de Curto Prazo Através da Similaridade entre Perfis de Consumo (Improvement of the Short Term Load Forecasting Through the Similarity Among Consumption Profiles)

Fioravante Ferro (humbertoff@celesc.com.br)1, Raul Wazlawick (raul@inf.ufsc.br)2, Rogério Bastos (rogerio@inf.ufsc.br)2, Cláudio Oliveira (claudiom@inf.ufsc.br)2

1Centrais Elétricas de Santa Catarina Distribuição SA
2Universidade Federal de Santa Catarina

This paper appears in: Revista IEEE América Latina

Publication Date: Sept. 2009
Volume: 7,   Issue: 5 
ISSN: 1548-0992

In order to achieve high quality standards in electrical power systems, utility companies rely upon load forecasting to accomplish critical activities such as optimal dynamic dispatch and smart performance in the power wholesale market. Several works propose hybrid intelligent forecasting models to deal with the dynamic and non-linear characteristics of the load at a relatively high computational cost. While such approaches give emphasis to the forecasting itself, this paper presents a procedure to detect similarities among distinct consumption profiles. Empirical results show that similar profiles share similar sets of relevant predictors. As finding similarities among profiles is less costly than finding the set of relevant predictors from scratch, a new parameter selection method is proposed. Such method is employed to build some neural forecasters with a marked improvement in the learning time.

Index Terms:
load forecasting, features extraction, power systems   

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