New Stochastic Model for One-Month-Ahead Wind Power Forecasting Shows Promising Accuracy
Researchers have developed a one-month-ahead probabilistic wind power forecasting framework operating at ten-minute resolution, using Weibull-stationary stochastic differential equations and a heteroskedastic Kalman filter. The method was tested on January 2021 data from a single wind turbine at Kelmarsh Wind Farm in the UK, with simulated power distributions falling within 1.4% of rated capacity in Wasserstein distance. The approach provides decision-relevant probabilistic inputs for grid operators, though the authors note it stops short of solving downstream optimization problems such as reserve scheduling or energy storage.
A preprint submitted to arXiv presents a conditional probabilistic forecasting framework designed to predict wind power output one month ahead at ten-minute intervals. The method estimates monthly Weibull distribution parameters from serially dependent SCADA wind-speed data, applies a Godambe covariance correction for serial dependence, and forecasts those parameters using a heteroskedastic Kalman filter on a bivariate VAR(1) state-space model. Three stochastic differential equation formulations for wind speed are constructed and compared: an Ornstein-Uhlenbeck-Weibull transform, a Fokker-Planck drift-first model, and a Fokker-Planck diffusion-first model. Applied to a Senvion MM92 turbine at Kelmarsh Wind Farm, all three SDE variants produced statistically indistinguishable probabilistic accuracy, with mean Continuous Ranked Probability Score (CRPS) values between 1.569 and 1.575 m/s. The diffusion-first model was selected as preferred because it runs approximately seven times faster than the OU-Weibull formulation. Monthly energy-yield bias was approximately -7.3% for the examined period, and exceedance-probability errors remained below 1.6 percentage points across most of the power range, with slightly higher errors near rated capacity.
What's missing
The study is evaluated on a single turbine over a single month (January 2021), leaving open questions about generalizability across seasons, turbine types, sites with different wind regimes, and multi-turbine or farm-level aggregation. The authors themselves flag full marginalisation over the Kalman predictive law of the Weibull parameters as an unresolved extension. The -7.3% monthly energy-yield bias is reported without a benchmark comparison to existing forecasting methods, making it difficult to assess relative performance. The paper has not yet undergone peer review.
What different sources said
- arXiv physicsCenter
Weibull-Stationary Stochastic Differential Equations for Conditional Long-Horizon Wind Power Forecasting
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