Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation

• URL: http://arxiv.org/abs/2106.04656v1
• Date: Fri, 4 Jun 2021 19:15:53 GMT
• Title: Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation
• Authors: Farzad Karami, Nasser Kehtarnavaz, Mario Rotea
• Abstract summary: A probabilistic neural network with Monte Carlo dropout is considered to quantify the model uncertainty of the power curve estimation. The developed network captures both model and noise uncertainty which is found to be useful tools in assessing performance.
• Score: 3.4376560669160385
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Enabling Uncertainty Estimation in Iterative Neural Networks [49.56171792062104]
  We develop an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles. We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes.
  arXiv  Detail & Related papers  (2024-03-25T13:06:31Z)
• Tractable Function-Space Variational Inference in Bayesian Neural Networks [72.97620734290139]
  A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters. We propose a scalable function-space variational inference method that allows incorporating prior information. We show that the proposed method leads to state-of-the-art uncertainty estimation and predictive performance on a range of prediction tasks.
  arXiv  Detail & Related papers  (2023-12-28T18:33:26Z)
• Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification [2.126171264016785]
  We use physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm. The developed models for regression of the power, torque, and power coefficient showed great accuracy for both real data and physical equations governing the system.
  arXiv  Detail & Related papers  (2023-07-27T07:58:38Z)
• Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature [0.0]
  A power curve is a key part of structural health monitoring in wind turbines. Many probabilistic power curve models have a key limitation in that they are not physically meaningful. This paper investigates the use of two bounded Gaussian Processes in order to produce physically meaningful probabilistic power curve models.
  arXiv  Detail & Related papers  (2022-09-30T16:45:15Z)
• Variational Neural Networks [88.24021148516319]
  We propose a method for uncertainty estimation in neural networks called Variational Neural Network (VNN) VNN generates parameters for the output distribution of a layer by transforming its inputs with learnable sub-layers. In uncertainty quality estimation experiments, we show that VNNs achieve better uncertainty quality than Monte Carlo Dropout or Bayes By Backpropagation methods.
  arXiv  Detail & Related papers  (2022-07-04T15:41:02Z)
• Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data [0.0]
  We consider the use of Bayesian Neural Networks (BNNs) to predict wind speed and direction. For our dataset, we use observations recorded at the FINO1 research platform in the North Sea. We conclude that the accuracy and uncertainty of the wind speed and direction predictions made by our network are unaffected by the construction of the nearby Alpha Ventus wind farm.
  arXiv  Detail & Related papers  (2022-06-14T15:57:14Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Wind Power Projection using Weather Forecasts by Novel Deep Neural Networks [0.0]
  Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data. The paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves.
  arXiv  Detail & Related papers  (2021-08-22T17:46:36Z)
• Neural network interpretability for forecasting of aggregated renewable generation [0.0]
  There is a need for aggregated prosumers to predict solar power generation. This paper presents two interpretable neural networks to solve the problem.
  arXiv  Detail & Related papers  (2021-06-19T11:50:26Z)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.