Solar irradiation may be predicted based on input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; for example, wherein the terrestrial sky images are crowd sourced from the plurality of devices. A model may be generated that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken. A signal representing the solar irradiation predicted by the model is output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of determining solar irradiation, the method performed by at least on hardware processor, comprising: receiving as input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices participating as community members; characterizing clouds in the terrestrial sky images; predicting future shape and position of the clouds in image pixels of the terrestrial sky images by executing a first machine learning model trained to predict cloud positions based on cloud speed, wind speed and direction; generating a second machine learning model that predicts solar irradiation in a geographic area based on cloud shape and position; executing the second machine learning model to predict the solar irradiation based on the future shape and position of the clouds predicted by the first machine learning model; and outputting a signal representing the solar irradiation. 2. The method of claim 1, further comprising: determining cloudiness index associated with the terrestrial sky images; and executing a time series model that predicts another solar irradiation based on the cloudiness index. 3. The method of claim 2, wherein the solar irradiation and the another solar irradiation are combined based on weights to provide a weighted solar irradiation combination. 4. The method of claim 3, further comprising: determining solar power based on the weighted solar irradiation combination and solar panel characteristics associated with a solar panel in the geographic area. 5. The method of claim 1, wherein the terrestrial sky images are crowd sourced from a community network comprising the plurality of devices. 6. The method of claim 1, wherein the method is performed at a central service platform receiving the terrestrial sky images uploaded from the plurality of devices. 7. The method of claim 1, wherein the method is performed by one of the plurality of devices sharing the terrestrial sky images from the plurality of devices. 8. The method of claim 1, wherein the signal is provided to an entity not involved in acquiring the terrestrial sky images. 9. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of determining solar irradiation, the method comprising: receiving as input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; generating a model that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken; executing the model to predict the solar irradiation; and outputting a signal representing the solar irradiation. 10. The computer readable storage medium of claim 9, further comprising: determining solar power based on the solar irradiation and solar panel characteristics associated with a solar panel in the geographic area. 11. The computer readable storage medium of claim 9, wherein the terrestrial sky images are crowd sourced from a community network comprising the plurality of devices. 12. The computer readable storage medium of claim 9, wherein the method is performed at a central service platform receiving the terrestrial sky images uploaded from the plurality of devices. 13. The computer readable storage medium of claim 9, wherein the method is performed by one of the plurality of devices. 14. The computer readable storage medium of claim 9, wherein the signal is provided to an entity not involved in acquiring the terrestrial sky images. 15. The computer readable storage medium of claim 9, wherein the model comprises a machine learning model, and generating the model comprises training the model to learn to predict solar irradiation based on the input terrestrial sky images. 16. The computer readable storage medium of claim 9, wherein the model comprises a time-series model. 17. A system of determining solar irradiation, comprising: a hardware processor operable to receive as input terrestrial sky images comprising cloud images, the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices; the hardware processor further operable to generate a model that predicts solar irradiation in a geographic area based on the input terrestrial sky images and the geographic locations from where the terrestrial sky images were taken, the geographic locations determined based on cloud-sun line of vision angles; the hardware processor further operable to execute the model to predict the solar irradiation; and the hardware processor further operable to output a signal representing the solar irradiation. 18. The system of claim 17, further comprising: a storage device coupled to the hardware processor, the storage device storing the terrestrial sky images taken from a plurality of geographic locations by a plurality of devices, wherein the terrestrial sky images are crowd sourced from a community network comprising the plurality of devices. 19. The system of claim 17, wherein the hardware processor is further operable to determine solar power based on the solar irradiation and solar panel characteristics associated with a solar panel in the geographic area. 20. The system of claim 17, wherein the model comprises a machine learning model.