A new strategy for predicting short-term wind speed using soft computing models
Wind power prediction is a widely used tool for the large-scale integration of intermittent wind-powered generators into power systems. Given the cubic relationship between wind speed and wind power, accurate forecasting of wind speed is imperative for the estimation of future wind power generation...
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Published in | Renewable & sustainable energy reviews Vol. 16; no. 7; pp. 4563 - 4573 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Kidlington
Elsevier Ltd
01.09.2012
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | Wind power prediction is a widely used tool for the large-scale integration of intermittent wind-powered generators into power systems. Given the cubic relationship between wind speed and wind power, accurate forecasting of wind speed is imperative for the estimation of future wind power generation output. This paper presents a performance analysis of short-term wind speed prediction techniques based on soft computing models (SCMs) formulated on a backpropagation neural network (BPNN), a radial basis function neural network (RBFNN), and an adaptive neuro-fuzzy inference system (ANFIS). The forecasting performance of the SCMs is augmented by a similar days (SD) method, which considers similar historical weather information corresponding to the forecasting day in order to determine similar wind speed days for processing. The test results demonstrate that all evaluated SCMs incur some level of performance improvement with the addition of SD pre-processing. As an example, the SD+ANFIS model can provide up to 48% improvement in forecasting accuracy when compared to the individual ANFIS model alone. |
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Bibliography: | http://dx.doi.org/10.1016/j.rser.2012.05.042 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1364-0321 1879-0690 |
DOI: | 10.1016/j.rser.2012.05.042 |