Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

TL;DR

This paper introduces a probabilistic neural network using Monte Carlo dropout to quantify both model and data uncertainties in wind power estimation, improving accuracy with minimal additional computational cost.

Contribution

It presents a novel probabilistic neural network that estimates both epistemic and aleatoric uncertainties in wind power prediction, outperforming existing methods.

Findings

The proposed network captures both model and noise uncertainty.

It achieves superior prediction accuracy compared to existing models.

The approach adds minimal computational complexity.

Abstract

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.