Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation
Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation
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DOI：
10.1109/dcas53974.2022.9845651
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Farzad Karami;N. Kehtarnavaz;M. Rotea
中科院分区：
文献类型：
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作者：
Farzad Karami;N. Kehtarnavaz;M. Rotea
每年都有越来越多的风电场加入电网，以产生可持续能源。风力涡轮机的功率曲线显示了发电功率与风速之间的关系，在评估风电场的性能方面起着重要作用。神经网络已被用于功率曲线估计。然而，他们不产生他们的输出的信心措施，除非计算禁止贝叶斯方法使用。在本文中，一个概率神经网络与蒙特卡罗被认为是量化的模型或认知的不确定性的功率曲线估计。这种方法提供了一个最小的增加计算复杂性，从而评估时间。此外，通过添加概率损失函数，估计数据中的噪声或任意不确定性。开发的网络捕获模型和噪声的不确定性，这被发现是有用的工具，在评估性能。此外，开发的网络与现有的跨公共领域数据集显示上级性能的预测精度方面进行了比较。所获得的结果表明，开发的网络提供了量化的不确定性，同时保持准确的功率估计。
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.