The experiments conducted on the wind data provided by the European Centre for Medium-range Weather Forecasts show that 1% of the data is sufficient to reconstruct the other 99% with an average amplitude error of less than 0.5 m/s and an average angular error of less than 5 degrees. In a nutshell, our method provides an approach where a portion of the data is used as a proxy to estimate the measurements over the entire domain based only on a few measurements. In our study, we compare several machine learning techniques, namely: linear regression, K-nearest neighbours, decision trees and a neural network, and investigate the impact of sensor placement on the quality of the reconstruction. While methods provide comparable results the results show that sensor placement plays an important role. Thus, we propose that intelligent location selection for sensor placement can be done using k-means, and show that this indeed leads to increase in accuracy as compared to random sensor placement.
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机译：在欧洲中距离预测中心提供的风数据上进行的实验表明，1％的数据足以重建其他99％，平均幅度误差小于0.5 m / s，平均角度误差小于5度。简而言之，我们的方法提供了一种方法，其中一部分数据用作仅基于几个测量来估计整个域上的测量的代理。在我们的研究中，我们比较了几种机器学习技术，即：线性回归，k最近邻居，决策树和神经网络，并研究传感器放置对重建质量的影响。虽然方法提供了可比结果，但结果表明传感器放置起着重要作用。因此，我们提出了可以使用K-means进行传感器放置的智能位置选择，并且表明与随机传感器放置相比，这确实导致精度增加。
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