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The insulation paper loss of life (LOL) of a distribution transformer (DT) is largely determined by its winding hot-spot temperature. It is mainly estimated via the transformer load, ambient temperature, and related physical parameters. Fortunately, advanced metering infrastructure (AMI) can provide load profiles of DTs, allowing a cost-effective LOL estimation solution. However, the AMI dataset contains measurement errors that significantly reduce LOL estimation accuracy. A forecasting method is needed to recover the erroneous AMI data. This is a challenging problem, as the load profiles of DTs are nonstationary and volatile. We propose an ensemble of stacked long short-term memory (ES-LSTM) networks to simultaneously capture load consumption patterns of DTs and their correlations. The ES-LSTM consists of two independent LSTM networks and a feed-forward neural network (FFNN) stacked on top of them. A two-stage training procedure is applied. In the first stage, two LSTM networks are trained to capture the daily or weekly load patterns. In the second stage, the FFNN is trained to optimally combine the last hidden outputs of the two LSTM networks. Field verification is conducted using real AMI data obtained from an urban distribution utility in China. The test results confirmed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Traditional remote sensing observation can only derive chlorophyll-a concentration in surface water (Chlasurf), which is insufficient for water quality monitoring or biogeochemical applications. In this paper, a column-integrated biomass (CIB) estimation algorithm was established in Lake Dianchi, China, and the CIB was estimated by Ocean and Land Color Instrument (OLCI) data from 2016 to 2018. First, the results show that the method is applicable to CIB estimation in nonbloom conditions [mean absolute percentage error (MAPE) = 10.55%, mean ratio (MR) = 0.996]. On the one hand, Chlasurf could be estimated using a universal model of Chla (UMOC) estimation for case-II waters; on the other hand, CIB could be obtained from Chlasurf according to the linear relationship between the Chlasurf and CIB. Second, both the spatial and temporal variabilities of Chlasurf are larger than those of CIB, which indicates that the biomass of the whole lake is relatively stable from day to day. During algal bloom periods, the CIB at nonbloom areas tends to decrease, suggesting that algae from nonbloom regions have migrated to bloom regions. Third, wind speed (WS) is the most critical meteorological factor affecting the water surface biomass, compared with other parameters. The average percentage of surface-to-total biomass in nonbloom conditions is about 7% when the WS is less than 2 m/s. [ABSTRACT FROM AUTHOR]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]