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.
Highlights
Renewable energy utilization mainly wind power generation has acquired magnificent considerations in eyecatching number of countries following the adoption of the Kyoto protocol environmental convention
The wind turbine is modeled by a PSOANFIS black box to develop a relationship between the predicted numerical weather prediction (NWP) meteorological variables and the actual wind speed measurement recorded by the wind farm SCADA system
adaptive neuro-fuzzy inference system (ANFIS) model is developed to map the wind turbine wind speed vs. wind power characteristics based on the real operational conditions
Summary
Renewable energy utilization mainly wind power generation has acquired magnificent considerations in eyecatching number of countries following the adoption of the Kyoto protocol environmental convention. Regarding the available research works in the area, new forecasting approaches and techniques of input– output data manipulations are still in demand in order to enhance prediction accuracy and decrease the uncertainty in wind power forecasting, while keeping practically acceptable computation time. This objective leads to the new double-stage hybrid approach proposed in this research paper to utilize both statistical (wind farm SCADA records) and physical (NWP meteorological variables) data sources for achieving an effective and more accurate short-term wind power forecaster model.
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