# Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

arXiv: https://arxiv.org/abs/2106.04656 · HTML: https://mlsanity.com/paper/2106.04656/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Concrete Dropout](https://mlsanity.com/paper/1705.07832/) (2017)
- [Dropout as a Bayesian Approximation: Appendix](https://mlsanity.com/paper/1506.02157/) (2015)
- [A parametric model for wind turbine power curves incorporating environmental conditions](https://mlsanity.com/paper/1909.13780/) (2019)

## Built on by (most cited first, 2 of 2)

- [Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data](https://mlsanity.com/paper/2206.08953/) (2022)
- [A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting](https://mlsanity.com/paper/2605.22390/) (2026)

## Abstract (arXiv)

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.