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Each year a growing number of wind farms are being added to power grids togenerate electricity. The power curve of a wind turbine, which exhibits therelationship between generated power and wind speed, plays a major role inassessing the performance of a wind farm. Neural networks have been used forpower curve estimation. However, they do not produce a confidence measure fortheir output, unless computationally prohibitive Bayesian methods are used. Inthis paper, a probabilistic neural network with Monte Carlo dropout isconsidered to quantify the model (epistemic) uncertainty of the power curveestimation. This approach offers a minimal increase in computational complexityover deterministic approaches. Furthermore, by incorporating a probabilisticloss function, the noise or aleatoric uncertainty in the data is estimated. Thedeveloped network captures both model and noise uncertainty which is found tobe useful tools in assessing performance. Also, the developed network iscompared with existing ones across a public domain dataset showing superiorperformance in terms of prediction accuracy.

Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation