Wind Power Production Prediction Using Machine Learning Based Image Processing

A computer-implemented method, comprising:

• determining a training power curve image comprising a plurality of pixels that represents power production by a plurality of training wind turbines of a training wind farm as a function of wind speed;
• determining, by a machine learning (ML) encoder model, a training latent representation of attributes of the training wind farm based on processing the training power curve image by the ML encoder model;
• determining, by an ML decoder model, a reconstruction of the training power curve image based on processing the training latent representation by the ML decoder model;
• determining a loss value based on comparing (i) the reconstruction of the training power curve image to (ii) the training power curve image;
• and
• adjusting one or more parameters of the ML encoder model based on the loss value.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations comprising:
• determining a power curve image comprising a plurality of pixels that represents power production by a plurality of wind turbines of a wind farm as a function of wind speed;
• determining, by a machine learning (ML) encoder model, a latent representation of attributes of the wind farm based on processing the power curve image by the ML encoder model;
• obtaining an expected weather data corresponding to a future time;
• determining, based on the latent representation and the expected weather data, an expected power production by the wind farm at the future time;
• and
• generating an output comprising the expected power production.