SPATIO-TEMPORAL SHORT-TERM WIND FORECAST: A CALIBRATED REGIME-SWITCHING METHOD.

Area of Science:

• Renewable Energy Systems
• Meteorological Forecasting
• Statistical Modeling

Background:

• Accurate short-term wind energy forecasting is vital for power grid stability.
• Existing statistical models struggle with near-future wind variations.
• Wind conditions exhibit significant spatio-temporal variations and nonstationarity.

Purpose of the Study:

• To develop a novel forecasting method that addresses limitations of current statistical models.
• To improve the accuracy of short-term wind speed and power predictions.
• To enhance the integration of wind energy into power grids.

Main Methods:

• Proposed a calibrated regime-switching method incorporating regime-dependent calibration and runlength.
• Modeled calibration as a function of wind regime and time since last regime change.
• Accounted for spatio-temporal dependencies, wind transport effects, and nonstationarity.

Main Results:

• The calibrated regime-switching method significantly improved wind speed forecasting accuracy.
• The model demonstrated superior performance in predicting wind power output.
• Achieved a wide margin of improvement over existing forecasting techniques.

Conclusions:

• The calibrated regime-switching method offers a substantial advancement in wind energy forecasting.
• This approach effectively corrects bias from out-of-sample wind behavior variations.
• Enhanced forecasting accuracy supports more reliable wind energy integration into power grids.