Each year a growing number of wind farms are being added to power grids to generate sustainable energy. 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 or epistemic uncertainty of the power curve estimation. This approach offers a minimal increase in computational complexity and thus evaluation time. Furthermore, by adding a probabilistic loss function, the noise or aleatoric uncertainty in the data is estimated. The developed network captures both model and noise uncertainty which are found to be useful tools in assessing performance. Also, the developed network is compared with the existing ones across a public domain dataset showing superior performance in terms of prediction accuracy. The results obtained indicate that the developed network provides the quantification of uncertainty while maintaining accurate power estimation.
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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules
Bayesian neural networks are powerful inference
methods by accounting for randomness in the data and the
network model. Uncertainty quantification at the output of
neural networks is critical, especially for applications such as autonomous
driving and hazardous weather forecasting. However,
approaches for theoretical analysis of Bayesian neural networks
remain limited. This paper makes a step forward towards
mathematical quantification of uncertainty in neural network
models and proposes a cubature-rule-based computationally efficient
uncertainty quantification approach that captures layerwise
uncertainties of Bayesian neural networks. The proposed
approach approximates the first two moments of the posterior
distribution of the parameters by propagating cubature points
across the network nonlinearities. Simulation results show that
the proposed approach can achieve more diverse layer-wise
uncertainty quantification results of neural networks with a fast
convergence rate.
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- Award ID(s):
- 1903466
- NSF-PAR ID:
- 10161394
- Date Published:
- Journal Name:
- the International Joint Conference on Neural Networks
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
