Modeling and calculating normalized aggregate power of renewable energy source stations

A system comprising:

• a memory;
• and
• a processor in communication with the memory, the processor configured to perform a method comprising
• receiving, by a computer system, location data of a plurality of renewable energy source stations in a geographic region, power output time series data of each of the plurality of renewable energy source stations, weather time series data of weather parameters corresponding to the plurality of renewable energy source stations, and aggregate power output time series data for the geographic region,
• clustering, by the computer system, the plurality of renewable energy source stations according to a clustering metric, resulting in clusters of renewable energy source stations,
• for each of the clusters, designating, by the computer system, a renewable energy source station among the plurality of renewable energy source stations to be a representative renewable energy source station of the each of the clusters based on at least an aggregate power value among the aggregate power output time series data,
• for the each of the clusters, determining, by the computer system, a set of features corresponding to the representative renewable energy source station of the each of the clusters, resulting in a determined set of features of the each of the clusters,
• for the each of the clusters, executing, by the computer system, a set of logical operations normalizing the aggregate power value to the representative renewable energy source station of the each of the clusters with respect to a maximum of aggregate power values in the aggregate power output time series data, resulting in a normalized aggregate output power value of the representative renewable energy source station of the each of the clusters,
• for the each of the clusters, executing, by the computer system, a set of logical operations learning a regression model of the each of the clusters based on the determined set of features of the each of the clusters and the normalized aggregate output power value of the representative renewable energy source station of the each of the clusters, resulting in learned regression models of the clusters of renewable energy source stations,
• for the each of the clusters, executing, by the computer system, a set of logical operations de-normalizing a normalized aggregate output power model of the each of the clusters, among the learned regression models, with respect to a maximum possible power value, resulting in a de-normalized aggregate output power model of the each of the clusters,
• executing, by the computer system, a set of logical operations combining the learned regression models of the clusters of renewable energy source stations with respect to the de-normalized aggregate output power model of the each of the clusters, resulting in a combined model for aggregate power of the plurality of renewable energy source stations, and
• executing, by the computer system, a set of logical operations applying the combined model to the location data, the power output time series data, the weather time series data, and power output of representative renewable energy source stations for a particular day, among the power output time series data, resulting in a total aggregate power value of the plurality of renewable energy source stations for the particular day.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• receiving, by a computer system, location data of a plurality of renewable energy source stations in a geographic region, power output time series data of each of the plurality of renewable energy source stations, weather time series data of weather parameters corresponding to the plurality of renewable energy source stations, and aggregate power output time series data for the geographic region;
• clustering, by the computer system, the plurality of renewable energy source stations according to a clustering metric, resulting in clusters of renewable energy source stations;
• for each of the clusters, designating, by the computer system, a renewable energy source station among the plurality of renewable energy source stations to be a representative renewable energy source station of the each of the clusters based on at least an aggregate power value among the aggregate power output time series data;
• for the each of the clusters, determining, by the computer system, a set of features corresponding to the representative renewable energy source station of the each of the clusters, resulting in a determined set of features of the each of the clusters;
• for the each of the clusters, executing, by the computer system, a set of logical operations normalizing the aggregate power value to the representative renewable energy source station of the each of the clusters with respect to a maximum of aggregate power values in the aggregate power output time series data, resulting in a normalized aggregate output power value of the representative renewable energy source station of the each of the clusters;
• for the each of the clusters, executing, by the computer system, a set of logical operations learning a regression model of the each of the clusters based on the determined set of features of the each of the clusters and the normalized aggregate output power value of the representative renewable energy source station of the each of the clusters, resulting in learned regression models of the clusters of renewable energy source stations;
• for the each of the clusters, executing, by the computer system, a set of logical operations de-normalizing a normalized aggregate output power model of the each of the clusters, among the learned regression models, with respect to a maximum possible power value, resulting in a de-normalized aggregate output power model of the each of the clusters;
• executing, by the computer system, a set of logical operations combining the learned regression models of the clusters of renewable energy source stations with respect to the de-normalized aggregate output power model of the each of the clusters, resulting in a combined model for aggregate power of the plurality of renewable energy source stations;
• and
• executing, by the computer system, a set of logical operations applying the combined model to the location data, the power output time series data, the weather time series data, and power output of representative renewable energy source stations for a particular day, among the power output time series data, resulting in a total aggregate power value of the plurality of renewable energy source stations for the particular day.