Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting irradiance, comprising: learning a classification model using unsupervised learning based on historical irradiance data; updating the classification model using supervised learning based on an association between known cloudiness states and historical weather data; predicting a cloudiness state based on forecasted weather data; and predicting an irradiance using a regression model associated with the cloudiness state using a processor. 2. The method of claim 1, wherein the regression model is based on a difference between a predicted irradiance and a clear sky irradiance. 3. The method of claim 2, wherein the regression model is only based on a difference between the predicted irradiance and the clear sky irradiance if the cloudiness state is predicted to be clear or partially cloudy. 4. The method of claim 3, wherein the regression model is based on a predicted irradiance, without considering the clear sky irradiance, if the cloudiness state is predicted to be overcast. 5. The method of claim 1, wherein the classification model comprises one or more criteria and wherein predicting the cloudiness state comprises comparing predicted weather data to the one or more criteria. 6. The method of claim 5, wherein the one or more criteria are based on a measured or forecasted global horizontal irradiance. 7. The method of claim 5, wherein the one or more criteria are selected from a group consisting of a difference between average global horizontal irradiance (GHI) and average clear sky GHI, a difference between a clear sky irradiance and a measured maximum GHI, a length of a curve of solar irradiance plotted against time, and a standard deviation of a slope of a the curve of solar irradiance. 8. The method of claim 5, wherein the one or more criteria are based on direct normal irradiance and diffuse horizontal irradiance. 9. The method of claim 1, wherein the cloudiness state is predicted to be one from the group consisting of, “clear,” “overcast,” and, “partially cloudy.” 10. A non-transitory computer readable storage medium comprising a computer readable program for predicting irradiance, wherein the computer readable program when executed on a computer causes the computer to perform the steps of claim 1. 11. A method for predicting irradiance, comprising: learning a classification model using unsupervised learning based on historical irradiance data; updating the classification model using supervised learning based on an association between known cloudiness states and historical weather data by comparing the predicted weather data to one or more criteria that are based on a measured or forecasted global horizontal irradiance; predicting a cloudiness state based on forecasted weather data; and predicting an irradiance using a regression model associated with the cloudiness state using a processor, wherein the regression model is based on a difference between the predicted irradiance and the clear sky irradiance if the cloudiness state is predicted to be clear or partially cloudy, and wherein the regression model is based on a predicted irradiance without considering the clear sky irradiance if the cloudiness state is predicted to be overcast. 12. A system for predicting irradiance, comprising: an unsupervised learning module configured to learn a classification model using unsupervised learning based on historical irradiance data; a supervised learning module configured to update the classification model using supervised learning based on an association between known cloudiness states and historical weather data; a classification module configured to predict a cloudiness state based on forecasted weather data; and a prediction module comprising a processor configured to predict an irradiance using a regression model associated with the cloudiness state. 13. The system of claim 12, wherein the regression model is based on a difference between a predicted irradiance and a clear sky irradiance. 14. The system of claim 13, wherein the regression model is only based on a difference between the predicted irradiance and the clear sky irradiance if the cloudiness state is predicted to be clear or partially cloudy. 15. The system of claim 14, wherein the regression model is based on a predicted irradiance, without considering the clear sky irradiance, if the cloudiness state is predicted to be overcast. 16. The system of claim 12, wherein the classification model comprises one or more criteria and wherein predicting the cloudiness state comprises comparing predicted weather data to the one or more criteria. 17. The system of claim 16, wherein the one or more criteria are based on a measured or forecasted global horizontal irradiance. 18. The system of claim 16, wherein the one or more criteria are selected from a group consisting of a difference between average global horizontal irradiance (GHI) and average clear sky GHI, a difference between a clear sky irradiance and a measured maximum GHI, a length of a curve of solar irradiance plotted against time, and a standard deviation of a slope of a the curve of solar irradiance. 19. The system of claim 16, wherein the one or more criteria are based on direct normal irradiance and diffuse horizontal irradiance. 20. The system of claim 12, wherein the cloudiness state is predicted to be one from the group consisting of, “clear,” “overcast,” and, “partially cloudy.”