Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks

Hugo Tavares Vieira Gouveia, Ronaldo Ribeiro Barbosa De Aquino and Aida Araújo Ferreira
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Hugo Tavares Vieira Gouveia: Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50740-533, PE, Brazil
Ronaldo Ribeiro Barbosa De Aquino: Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50740-533, PE, Brazil
Aida Araújo Ferreira: Federal Institute of Education, Science and Technology of Pernambuco (IFPE), Recife 50740-545, PE, Brazil

Energies, 2018, vol. 11, issue 4, 1-19

Abstract: This article suggests the application of multiresolution analysis by Wavelet Transform—WT and Echo State Networks—ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine’s power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error—NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model—PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks—ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting.

Keywords: Echo State Network; neural networks; reservoir computing; wavelet transform; wind forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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