With the high wind penetration in the power system, accurate and reliable probabilistic wind power forecasting has become even more significant for the reliability of the power system. In this paper, an instance-based transfer learning method combined with gradient boosting decision trees (GBDT) is proposed to develop a wind power quantile regression model. Based on the spatial cross-correlation characteristic of wind power generations in different zones, the proposed model utilizes wind power generations in correlated zones as the source problems of instance-based transfer learning. By incorporating the training data of source problems into the training process, the proposed model successfully reduces the prediction error of wind power generation in the target zone. To prevent negative transfer, this paper proposes a method that properly assigns weights to data from different source problems in the training process, whereby the weights of related source problems are increased, while those of unrelated ones are reduced. Case studies are developed based on the dataset from the Global Energy Forecasting Competition 2014 (GEFCom2014). The results confirm that the proposed model successfully improves the prediction accuracy compared to GBDT-based benchmark models, especially when the target problem has a small training set while resourceful source problems are available.

In this paper, by utilizing corresponding load data of source zones, a two-layer transfer-learning-based short-term load forecasting (STLF) architecture is proposed to improve forecasting accuracy of load in target zone. In the inner layer, the latent parameter is introduced to represent the latent factors that resulting the differences in electricity consumption behavior between different zones. With the latent parameter as extra input, a latent parameter assisted model suitable for both the load data of target zone and all source zones is built. Variant weights are assigned to datasets according to their fitness to the latent parameter assisted model. To solve the weights, an iterative algorithm is developed in the outer layer. Case studies demonstrates that the proposed STLF architecture always improves the forecasting accuracy of classic STLF algorithms, especially when the load data of target zone is insufficient.

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art.

Superconducting magnetic levitation (maglev) is de-veloping rapidly in recent decades. Compared to traditional rail-ways, superconducting maglev is superior in aspects such as faster speed, less pollution and safer operation. The development of sec-ond generation (2G) high-temperature Superconducting (HTS) wires, which have higher in-field critical current density and me-chanical strength, has also improved the design performance of the superconducting maglev. This paper presents design and optimiza-tion processes of no-insulation magnets using 2G HTS wires for a prototype of linear propulsion system in the maglev. According to the design results, one of the coils in the magnets are manufactured for routine test including IC measurement and maglev-related tests including field quality comparison, charging characters like delay and loss, over-MMF test and mechanical vibration test simulating the running conditions of the maglev. The coil has no degradations or obvious changes of performances after the tests. And the test re-sults meet the expected requirements of the prototype.

The high-temperature superconducting (HTS) null-flux electrodynamic suspension (EDS) system is one of the most promising solutions for high-speed transportation. In this article, a 3-D model based on the finite-element method (FEM) is established to solve the electromagnetic forces in the HTS null-flux EDS system with the commercial software COMSOL Multiphysics. The current induced in the null-flux coils and the forces on the HTS magnets were studied and compared with the results from our dynamic circuit model. The two models agree with each other with a difference of less than 6%. The FEM model can deal with 3-D geometries, allowing the analysis of complex situations, such as the eddy current in the cryostat walls. Furthermore, the model can be readily extended to solve coupled problems, including the heat and mechanical behaviors of the HTS magnets and/or the null-flux coils, which are engineering concerns in real applications.