Missing Data Imputations for Upper Air Temperature at 24 Standard Pressure Levels over Pakistan Collected from Aqua Satellite

This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R2 ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

High-quality datasets are of paramount importance for the operation and planning of wind farms.However,the datasets collected by the supervisory control and data acquisition(SCADA)system may contain missing data due to various factors such as sensor failure and communication congestion.In this paper,a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder(CE),which consists of an encoder,a decoder,and a discriminator.Through deep convolutional neural networks,the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly.The proposed method can not only fully use the surrounding context information by the reconstructed loss,but also make filling data look real by the adversarial loss.In addition,the correlation among multiple missing attributes is taken into account by adjusting the format of input data.The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks,large valleys,and fast ramps.Moreover,the CE shows stronger generalization ability than traditional methods such as auto-encoder,K-means,k-nearest neighbor,back propagation neural network,cubic interpolation,and conditional generative adversarial network for different missing data scales.