Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation
This work addresses the need for reliable uncertainty measures in wind farm performance assessment, offering an incremental improvement over existing neural network methods.
The paper tackles the problem of quantifying uncertainty in wind power estimation by developing a probabilistic neural network that captures both model and noise uncertainty with minimal computational overhead, achieving superior prediction accuracy on a public dataset.
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.