Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/4/824 |
| _version_ | 1798039122592399360 |
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| author | Hugo Tavares Vieira Gouveia Ronaldo Ribeiro Barbosa de Aquino Aida Araújo Ferreira |
| author_facet | Hugo Tavares Vieira Gouveia Ronaldo Ribeiro Barbosa de Aquino Aida Araújo Ferreira |
| author_sort | Hugo Tavares Vieira Gouveia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:49:33Z |
| format | Article |
| id | doaj.art-92fe35bbbcd943b9ab778b02e65725db |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:49:33Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-92fe35bbbcd943b9ab778b02e65725db2022-12-22T04:01:17ZengMDPI AGEnergies1996-10732018-04-0111482410.3390/en11040824en11040824Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State NetworksHugo Tavares Vieira Gouveia0Ronaldo Ribeiro Barbosa de Aquino1Aida Araújo Ferreira2Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50740-533, PE, BrazilDepartment of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife 50740-533, PE, BrazilFederal Institute of Education, Science and Technology of Pernambuco (IFPE), Recife 50740-545, PE, BrazilThis 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.http://www.mdpi.com/1996-1073/11/4/824Echo State Networkneural networksreservoir computingwavelet transformwind forecasting |
| spellingShingle | Hugo Tavares Vieira Gouveia Ronaldo Ribeiro Barbosa de Aquino Aida Araújo Ferreira Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks Energies Echo State Network neural networks reservoir computing wavelet transform wind forecasting |
| title | Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks |
| title_full | Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks |
| title_fullStr | Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks |
| title_full_unstemmed | Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks |
| title_short | Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks |
| title_sort | enhancing short term wind power forecasting through multiresolution analysis and echo state networks |
| topic | Echo State Network neural networks reservoir computing wavelet transform wind forecasting |
| url | http://www.mdpi.com/1996-1073/11/4/824 |
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