Predicting solar power generation using semi-supervised learning

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.

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.