Jun Hu

We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model to probabilistic load forecasting using Monte Carlo dropout. Three public datasets are used to prove the effectiveness of the proposed model. Multiple test cases and comparison with existing models show that the proposed model provides accurate load forecasting results and has high generalization capability.

Abstract Dielectric polymers for electrostatic energy storage suffer from low energy density and poor efficiency at elevated temperatures, which constrains their use in the harsh-environment electronic devices, circuits, and systems. Although incorporating insulating, inorganic nanostructures into dielectric polymers promotes the temperature capability, scalable fabrication of high-quality nanocomposite films remains a formidable challenge. Here, we report an all-organic composite comprising dielectric polymers blended with high-electron-affinity molecular semiconductors that exhibits concurrent high energy density (3.0 J cm −3 ) and high discharge efficiency (90%) up to 200 °C, far outperforming the existing dielectric polymers and polymer nanocomposites. We demonstrate that molecular semiconductors immobilize free electrons via strong electrostatic attraction and impede electric charge injection and transport in dielectric polymers, which leads to the substantial performance improvements. The all-organic composites can be fabricated into large-area and high-quality films with uniform dielectric and capacitive performance, which is crucially important for their successful commercialization and practical application in high-temperature electronics and energy storage devices.

Three new integration algorithms for motor flux estimation are proposed in this paper. These algorithms are developed for use in high-performance sensorless AC motor drives. The first algorithm is used to elaborate the basic operating principle. The second one is designed for the drives that require a constant air-gap flux during operation. The third algorithm, in which an adaptive controller is used, can have wide industrial applications. The proposed algorithms can effectively solve the problems associated with pure integrators. These algorithms can be used to accurately measure the motor flux including its magnitude and phase angle over a wide speed range (1:100). The provenance of the algorithms is investigated, compared, and verified experimentally.

Abstract High‐temperature capability is critical for polymer dielectrics in the next‐generation capacitors demanded in harsh‐environment electronics and electrical‐power applications. It is well recognized that the energy‐storage capabilities of dielectrics are degraded drastically with increasing temperature due to the exponential increase of conduction loss. Here, a general and scalable method to enable significant improvement of the high‐temperature capacitive performance of the current polymer dielectrics is reported. The high‐temperature capacitive properties in terms of discharged energy density and the charge–discharge efficiency of the polymer films coated with SiO 2 via plasma‐enhanced chemical vapor deposition significantly outperform the neat polymers and rival or surpass the state‐of‐the‐art high‐temperature polymer nanocomposites that are prepared by tedious and low‐throughput methods. Moreover, the surface modification of the dielectric films is carried out in conjunction with fast‐throughput roll‐to‐roll processing under ambient conditions. The entire fabrication process neither involves any toxic chemicals nor generates any hazardous by‐products. The integration of excellent performance, versatility, high productivity, low cost, and environmental friendliness in the present method offers an unprecedented opportunity for the development of scalable high‐temperature polymer dielectrics.