Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

It is a challenge for the dynamic inspection of railway route for freight car transporting cargo that out-of-gauge. One possible way is using the inspection frame installed in the inspection train to simulate the whole procedure for cargo transportation, which costs a lot of manpower and material resources as well as time. To overcome the above problem, this paper proposes an augmented reality (AR) based dynamic inspection method for visualized railway routing of freight car with out-of-gauge. First, the envelope model of the dynamic moving train with out-of-gauge cargo is generated by using the orbital spectrum of the railway, and the envelope model is matched with a piece of homemade calibration equipment located on the position of the railway that needs to be inspected. Then, the structure from motion (SFM) algorithm is used to reconstruct the environment where the virtual envelope model occludes the buildings or equipment along the railway. Finally, the distance function is adopted to calculate the distance between the obstacle and the envelope of the freight car with out-of-gauge, determining whether the freight car can pass a certain line. The experimental results show that the proposed method performs well for the route selection of out-of-gauge cargo transportation with low cost, high precision, and high efficiency. Moreover, the digital data of the environments along the railway and the envelope of the freight car can be reused, which will increase the digitalization and intelligence for route selection of out-of-gauge cargo transportation.

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This work presents theoretical and numerical models for the backscattering of two-dimensional Rayleigh waves by an elastic inclusion, with the host material being isotropic and the inclusion having an arbitrary shape and crystallographic symmetry. The theoretical model is developed based on the reciprocity theorem using the far-field Green's function and the Born approximation, assuming a small acoustic impedance difference between the host and inclusion materials. The numerical finite element (FE) model is established to deliver a relatively accurate simulation of the scattering problem and to evaluate the approximations of the theoretical model. Quantitative agreement is observed between the theoretical model and the FE results for arbitrarily shaped surface/subsurface inclusions with isotropic/anisotropic properties. The agreement is excellent when the wavelength of the Rayleigh wave is larger than, or comparable to, the size of the inclusion, but it deteriorates as the wavelength gets smaller. Also, the agreement decreases with the anisotropy index for inclusions of anisotropic symmetry. The results lay the foundation for using Rayleigh waves for quantitative characterization of surface/subsurface inclusions, while also demonstrating its limitations.