Systems and methods for forecasting wind farm power generation are disclosed. Via use of a support vector machine (SVM) enhanced Markov model, short-term wind power generation forecasts may be generated. Exemplary approaches accurately account for wind ramp-up and ramp-down, as well as diurnal non-stationarity and seasonality of wind power generation. Via use of the disclosed forecasting approaches, utilities and grid managers can make improved decisions relating to electrical power generation and transmission, thus reducing costs and reducing pollution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for forecasting power generation in a wind farm, the method comprising: identifying, by a processor for forecasting wind farm power generation, relationships among wind turbines in the wind farm using minimum spanning trees; calculating, by the processor and using the minimum spanning trees, power output relationships among the wind turbines; creating, by the processor, a finite state space Markov chain forecast model for the wind turbines in the wind farm; creating, by the processor, a support vector machine (SVM) model for each state in the Markov chain; integrating, by the processor, the SVM model into the Markov model to generate a forecast of the wind farm power generation; and modifying at least one of a generation of electricity or a distribution of electricity based on the predicted power output of the wind farm. 2. The method of claim 1, wherein the forecast of the wind farm power generation is generated using the equation {circumflex over (P)}agSVM(t+1)=Pag(t)+Rj*k, wherein Pag(t)ε[Γk,Γk+1) is the current observed wind farm power generation, and wherein the corresponding forecast state S{circumflex over (k)} is the state satisfying {circumflex over (P)}agSVM(t+1)ε[Γ{circumflex over (k)},Γ{circumflex over (k)}+1). 3. The method of claim 2, wherein the forecast of the wind farm power generation is a distributional forecast, and wherein the distributional forecast is generated using the equation Pr ( P ag  ( t + 1 ) =  P ag , j  S  ( t ) =  S k , x  ( t ) ) =  { q k ; if   j = k ^ ; ( 1 - q k )  Q k j ∑ l ≠ k   Q kl , otherwise , wherein S(t) is the state of the Markov chain at time t and wherein x(t)εl is the observed wind farm power generation at time t. 4. The method of claim 3, further comprising generating, by the processor, a point forecast from the distributional forecast, and wherein the point forecast is generated using the equation P ^ ag  ( t + 1 ) = P ag , k ^  q k + ∑ j ≠ k ^   P ag , j  ( 1 - q k )  Q kj ∑ l ≠ k ^   Q kl . 5. The method of claim 1, wherein each minimum spanning tree: comprises a subset of the wind turbines in the wind farm; identifies a parent turbine for each wind turbine in the subset; and guarantees that each wind turbine in the subset is linked to a root turbine of the subset. 6. The method of claim 1, further comprising providing the forecast to at least one of an electric utility or a customer of an electric utility. 7. The method of claim 1, wherein the modifying the generation of electricity results in reduced greenhouse gas emissions associated with the generation of the electricity. 8. The method of claim 1, wherein the modifying the generation of electricity results in decreased costs associated with the generation of the electricity. 9. The method of claim 1, wherein the creation of the finite state space Markov chain forecast model utilizes historical data comprising at least one of historical data for wind farm power generation and historical data for wind speed associated with the wind farm. 10. The method of claim 1, wherein the creation of the finite state space Markov chain forecast model utilizes ramp trend information. 11. The method of claim 1, wherein the forecast of the wind farm power generation predicts the power output of the wind farm for a period of between about 5 minutes and 1 hour into the future.