Application of artificial intelligence techniques and statistical ensembling to forecast power output of a wind energy facility

A wind energy forecasting system, comprising:

• at least one computer processor operably coupled to at least one computer-readable storage medium having program instructions stored therein, the at least one computer processor configured to execute the program instructions to model weather variables representative of one or more meteorological conditions and generate specific power output forecasts for a wind energy facility in a plurality of data processing modules, the plurality of data processing modules including:
• an input data collection module configured to continually ingest weather data comprising the weather variables from one or more numerical weather prediction model runs, an actual power output data collection module configured to ingest data relative to a real-time and historical power output of the wind energy facility over a specified period of time;
• a plurality of neural networks heuristically built from the weather variables from the input data collection module and the historical power output from the actual power output data collection module, and trained to infer non-linear relationships between the weather variables and the historical power output of the wind energy facility to produce a specific power output forecast for each numerical weather prediction model for the meteorological conditions represented in the weather variables;
• an ensembling module configured to aggregate the specific power output forecasts from each numerical weather prediction model run, and a persistence power output forecast based on real-time power output, into an ensemble of members by continuously tracking statistical properties of each specific power output forecast to assign a weight for each specific power output forecast, the weight determined based on a minimum variance estimation;
• and
• a power production module configured to generate output data representative of a consensus power output forecast for the wind energy facility.

A method of forecasting power output of a wind energy facility, comprising:

• modeling weather variables from a plurality of numerical weather prediction models and actual power output data of wind energy facility, by:
• heuristically building one or more neural networks by training each neural network to infer non-linear relationships between the weather variables and the actual power output data of wind energy facility to produce a specific power output forecast for each numerical weather prediction model for the meteorological conditions represented in the weather variables;
• aggregating the numerical weather prediction models from which the specific power output forecasts are produced into an ensemble of numerical weather prediction model members;
• and
• statistically combining the specific power output forecast for each numerical weather predictive model with a real-time persistence power output forecast of the wind energy facility by assigning a weight to each specific power output forecast to produce a minimum variance estimate.