| Abstract: |
| Determination of the output power of wind generators is always associated with some uncertainties due to wind
speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient
operation. This can efficiently support transmission and distribution system operators and schedulers to improve
the power network control and management. In this paper, we propose a double stage hierarchical adaptive
neuro-fuzzy inference system (double-stage hybrid ANFIS) for short-term wind power prediction of a microgrid
wind farm in Beijing, China. The approach has two hierarchical stages. The first ANFIS stage employs numerical
weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and
turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted
next day’s wind speed by the first stage is applied to the second stage to forecast next day’s wind power. The
influence of input data dependency on prediction accuracy has also been analyzed by dividing the input data
into five subsets. The presented approach has resulted in considerable forecasting accuracy enhancements. The
accuracy of the proposed approach is compared with other three forecasting approaches and achieved the best
accuracy enhancement than all. |
| Key words: Energy management, Forecasting, Fuzzy logic, Microgrid, Neural network, Numerical weather prediction,Wind power |
| DOI：10.1186/s41601-017-0041-5 |
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| Fund: |
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