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The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the "change" strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes. [ABSTRACT FROM AUTHOR]

The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted this brain-liked neural network to a real-time prediction for power load. We applied the model in five cities (Tangshan, Langfang, Qinhuangdao, Chengde, Zhangjiakou) of north China. We experimented with the proposed model and Long Short-term Memory (LSTM) model and analyzed the predict results and real currents. The results show that the predictions conform to the reality; compared to LSTM, the HTM-based prediction model shows enhanced accuracy and stability. The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation. [ABSTRACT FROM AUTHOR]

The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the new energy consumption. This paper proposes a composite prediction framework (DC (DWT-DAE)-CNN) consisting of dual clustering and convolutional neural network to achieve day-ahead prediction of PV power. To avoid the temporal uncertainty of PV power and the high-dimensional complexity of numerical weather prediction, the raw data are processed by Discrete Wavelet Transform (DWT) and Deep Autoencoder respectively (DAE) to reduce the data redundancy. Secondly, a dual clustering pattern based on dynamic time warping distance clustering and Fuzzy C-Mean (FCM) clustering is proposed to progressively realize the dynamic characteristics of the power curve and numerical clustering of the weather information data. Finally, the data from PV plants in northeast China are used for validation. The results show that the annual average day-ahead prediction AR of the DC (DWT-DAE)-CNN model can reach 90.17%, which is better than other competing models. In addition, the dual clustering pattern performs better than other traditional clustering patterns with the same predictor. Using this method to predict the PV output power can provide better theoretical guidance for the stable and safe operation of grid-connected PV. • A dual clustering model based on numerical variation and curve morphology variation is proposed. • The power data and NWP data were processed separately using Discrete Wavelet Transform and Deep Autoencoder. • Building multiple convolutional neural network predictors for PV power day-ahead prediction. • The proposed model was shown to outperform the comparison model under different seasons. [ABSTRACT FROM AUTHOR]

We report optical observations of the millisecond pulsar binary system PSR J1622–0315 with the Lulin 1 m telescope in Taiwan and the Lijiang 2.4 m telescope in China between 2019 and 2021. The companion of the pulsar, which is of V ∼ 19 mag, showed ellipsoidal-distorted orbital variations in its light curves. The best-fit model to the light curves, with the binary code PHOEBE, gives a companion mass of 0.122 ± 0.006 M ⊙. This places PSR J1622–0315 in the spider-system subclass. We compared the properties of PSR J1622–0315 with other spider pulsar binaries for the scalings between the spin-down luminosity derived for the pulsar, irradiation luminosity of the companion, and X-ray luminosity of the binary. We find that pulsar irradiation in PSR J1622–0315 is insignificant and the irradiation luminosity of the transitional millisecond pulsars PSR J1023+0038 and PSR J1227–4853 are the highest among the redback systems. [ABSTRACT FROM AUTHOR]