  Each year a growing number of wind farms are being added to power grids to
generate electricity. The power curve of a wind turbine, which exhibits the
relationship between generated power and wind speed, plays a major role in
assessing the performance of a wind farm. Neural networks have been used for
power curve estimation. However, they do not produce a confidence measure for
their output, unless computationally prohibitive Bayesian methods are used. In
this paper, a probabilistic neural network with Monte Carlo dropout is
considered to quantify the model (epistemic) uncertainty of the power curve
estimation. This approach offers a minimal increase in computational complexity
over deterministic approaches. Furthermore, by incorporating a probabilistic
loss function, the noise or aleatoric uncertainty in the data is estimated. The
developed network captures both model and noise uncertainty which is found to
be useful tools in assessing performance. Also, the developed network is
compared with existing ones across a public domain dataset showing superior
performance in terms of prediction accuracy.
