A method for predicting solar power generation receives historical power profile data and historical weather micro-forecast data at a given location for a set of days. Based on power output features for the days, clusters are generated. A classification model that assigns a day to a generated cluster according to weather features is created. For each cluster, a regression model that takes as input weather features and outputs predicted solar power is built. A system includes a sensor for collecting meteorological data at a solar farm, a meter for measuring photovoltaic power output of the solar farm, and a computer processor for executing instructions to predict solar power generation at the solar farm according to the method disclosed, based on data from the sensor and the meter, for a predefined time period. Further instructions predict solar power generation at the solar farm based on a micro-forecast for the solar farm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for predicting photovoltaic solar power generation, the method comprising: receiving, by one or more processors, historical power profile data and historical weather micro-forecast data at a given location for a set of days; generating, by one or more processors, clusters from the set of days, the clusters corresponding to types of days, according to power output features of days of the set of days; creating, by one or more processors, a classification model that assigns a day to a generated cluster according to weather features of the day; and for a generated cluster, building, by one or more processors, a regression model that takes as input weather features of a day and outputs predicted solar power. 2. The method of claim 1, wherein historical weather micro-forecast data comprises measurements at specified time intervals of one or more of: direct normal irradiance, direct horizontal irradiance, diffuse horizontal irradiance, global horizontal irradiance, and solar zenith angle. 3. The method of claim 2, wherein the specified time intervals are hours. 4. The method of claim 1, wherein historical power output data comprises measurements of generated power output at specified time intervals. 5. The method of claim 4, wherein the specified time intervals are hours. 6. The method of claim 1, wherein generating clusters comprises using, by one or more processors, an unsupervised machine learning method. 7. The method of claim 6, wherein the unsupervised machine learning method is one of: k-means, two-step, or DBSCAN. 8. The method of claim 1, wherein the power output features comprise statistics based on averages of power measurements over specified time intervals. 9. The method of claim 8, wherein the statistics comprise one or more of: sum, mean, standard deviation, median, first quartile, and third quartile. 10. The method of claim 1, wherein creating a classification model comprises using, by one or more processors, a supervised machine learning method. 11. The method of claim 10, wherein the supervised machine learning method is one of: SVM, naïve Bayes, or decision trees. 12. The method of claim 1, wherein the weather features comprise statistics based on averages over specified time intervals of one or more of: direct normal irradiance, direct horizontal irradiance, diffuse horizontal irradiance, and global horizontal irradiance. 13. The method of claim 12, wherein the statistics comprise one or more of: sum, mean, standard deviation, median, first quartile, and third quartile. 14. The method of claim 1, wherein the regression model comprises one or more of: linear regression, a general linear model (GLM), and a neural network. 15. The method of claim 1, further comprising: receiving, by one or more processors, a weather micro-forecast for the given location for a range of days; determining, by one or more processors, the weather features for a day of the range of days from the weather micro-forecast; using, by one or more processors, the classification model to assign the day to a generated cluster, based on the determined weather features; and using, by one or more processors, the regression model for the generated cluster to compute a predicted power output for the day. 16. A system for predicting photovoltaic solar power generation of a solar farm, the system comprising: a sensor for collecting meteorological data in a region of a solar farm for use in a numerical weather model; a meter for measuring photovoltaic power output of the solar farm; one or more computer processors, one or more non-transitory computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive meteorological data collected from the sensor for use in a numerical weather model; program instructions to receive photovoltaic power output measurements measured by the meter corresponding to a predefined time period; program instructions to generate a weather micro-forecast for the time period in the region of the solar farm, based on the meteorological data and the numerical weather model; program instructions to produce a profile of photovoltaic power generated during the time period at the solar farm, based on the photovoltaic power output measurements; program instructions to receive the photovoltaic power profile and the weather micro- forecast at the solar farm for a set of days of the time period; program instructions to generate clusters from the set of days corresponding to types of days, according to power output features of days of the set of days; program instructions to create a classification model that assigns a day to a generated cluster according to weather features of the day; program instructions, for a generated cluster, to build a regression model that takes as input weather features of a day and outputs predicted solar power; program instructions to receive a weather micro-forecast for the solar farm for a future range of days; program instructions to determine the weather features for a day of the future range of days from the received weather micro-forecast; program instructions to use the classification model to assign the day to a generated cluster, based on the determined weather features; and program instructions to use the regression model for the generated cluster to compute a predicted power output for the day. 17. The system of claim 16, wherein historical weather micro-forecast data comprises hourly measurements of one or more of: direct normal irradiance, direct horizontal irradiance, diffuse horizontal irradiance, global horizontal irradiance, and solar zenith angle. 18. The system of claim 16, wherein historical power output data comprises hourly measurements of generated power output. 19. The system of claim 16, wherein program instructions to generate clusters comprises program instructions to use an unsupervised machine learning method. 20. The system of claim 16, wherein the power output features comprise statistics based on average hourly values of power measurements. 21. The system of claim 16, wherein program instructions to create a classification model comprise program instructions to use a supervised machine learning method. 22. The system of claim 16, wherein the weather features comprise statistics based on hourly averages of one or more of: direct normal irradiance, direct horizontal irradiance, diffuse horizontal irradiance, and global horizontal irradiance. 23. A computer program product for predicting photovoltaic solar power generation, the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to receive historical power profile data and historical weather micro-forecast data at a given location for a set of days; program instructions to generate clusters from the set of days corresponding to types of days, according to power output features of days of the set of days; program instructions to create a classification model that assigns a day to a generated cluster according to weather features of the day; and program instructions, for a generated cluster, to build a regression model that takes as input weather features of a day and outputs predicted solar power. 24. The computer program product of claim 23, further comprising: program instructions to receive a weather micro-forecast for the given location for a range of days; program instructions to determine the weather features for a day of the range of days from the weather micro-forecast; program instructions to use the classification model to assign the day to a generated cluster, based on the determined weather features; and program instructions to use the regression model for the generated cluster to compute a predicted power output for the day.