Hybrid Approach combining SARIMA and Neural Networks for multi-step ahead wind speed forecasting in Brazil
Item
-
Tipo do ITEM
-
Artigo Ciêntifico
-
Título do Artigo
-
Hybrid Approach combining SARIMA and Neural Networks for multi-step ahead wind speed forecasting in Brazil
-
Descrição
-
ABSTRACT This paper proposes a hybrid approach based on SARIMA and Neural Networks for multi-step
ahead wind speed forecasting using explanatory variables. In the proposed model, explanatory variables are
first predicted, and wind speed forecasting is performed taking into account these forecasted values and wind
speed historical series. The multi-step ahead forecasting is achieved recursively, by using the first forecasted
value as input to obtain the next forecasting value. The proposed approach is tested using historical records
of meteorological data collected from two real-world locations in Brazil. In order to demonstrate accuracy
and effectiveness of the proposed approach, the results are compared with other techniques such as Neural
Networks, SARIMA and SARIMA+wavelet. Simulation results reveal that the proposed hybrid forecasting
method outperforms these popular algorithms for different forecasting horizons with higher accuracy.
INDEX TERMS SARIMA model, explanatory variable selection, multi-step ahead, neural networks, wind
speed forecasting
-
Abstract
-
ABSTRACT This paper proposes a hybrid approach based on SARIMA and Neural Networks for multi-step
ahead wind speed forecasting using explanatory variables. In the proposed model, explanatory variables are
first predicted, and wind speed forecasting is performed taking into account these forecasted values and wind
speed historical series. The multi-step ahead forecasting is achieved recursively, by using the first forecasted
value as input to obtain the next forecasting value. The proposed approach is tested using historical records
of meteorological data collected from two real-world locations in Brazil. In order to demonstrate accuracy
and effectiveness of the proposed approach, the results are compared with other techniques such as Neural
Networks, SARIMA and SARIMA+wavelet. Simulation results reveal that the proposed hybrid forecasting
method outperforms these popular algorithms for different forecasting horizons with higher accuracy.
INDEX TERMS SARIMA model, explanatory variable selection, multi-step ahead, neural networks, wind
speed forecasting
-
Língua do arquivo
-
inglês
-
Data da Publicação
-
Ano 2017
-
Palavra-chave
-
SARIMA model
-
explanatory variable selection
-
multi-step ahead
-
neural networks
-
wind speed forecasting
-
Autores
-
David B. Alencar
-
Carolina M. Affonso
-
Roberto C. L. Oliveira
-
Jose C. R. Filho
-
Local
-
ITEGAM - Manaus, 2017
