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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | de Aquino, R.R.B. Gouveia, H.T.V. Lira, M.M.S. Ferreira, A.A. Neto, O.N. Carvalho, M.A. |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Electrical Engineering, Federal University of Pernambuco (UFPE), Recife, Brazil (de Aquino, R.R.B.; Gouveia, H.T.V.; Lira, M.M.S.; Ferreira, A.A.; Neto, O.N.; Carvalho, M.A.) |
| Abstract | Wind forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This work proposes and develops models to forecast hourly average wind speeds and wind power generation based on Artificial Neural Networks, Fuzzy Logic and Wavelets. The models were adjusted for forecasting with variable steps up to twenty-four hours ahead. The gain of some of the developed models in relation to the reference models was of approximately 80% for forecasts in a period of one hour ahead. The results showed that a wavelet analysis combined with artificial intelligence tools provides more reliable forecasts than those obtained with the reference models, especially for forecasts in a period of 1 to 6 hours ahead. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1038438 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252526 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Predictive models Wind speed Wind forecasting Forecasting Wind power generation Training Mathematical model Wavelet Transforms Wind Energy Artificial Intelligence Fuzzy Logic Wind Forecasting Neural Networks Time Series Analysis |
| Content Type | Text |
| Resource Type | Article |
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