RESUMEN
The forecast of clean energy power generation is of major prominence to energy structure adjustment and the realization of sustainable economic development in China. In order to scientifically predict clean energy power generation data, a structure-adaptive nonlinear grey Bernoulli model submitted to the new information priority criterion (abbreviated as IANGBM) is established. Firstly, an improved conformable fractional accumulation operator that conforms to the priority of new information is proposed, which can effectively extract the information from small samples. Then, IANGBM is derived from the Bernoulli differential equation, and the perturbation bound theory proves that this model is suitable for the analysis of small sample data. In addition, the grey wolf optimization algorithm is utilized to optimize the model parameters to make the model more adaptable and generalized. To verify the superiority of the model, two cases consisting of wind and nuclear power generation prediction are implemented by comparing eight benchmark models involving IANGBM, GM, FGM, FANGBM, LR, SVM, BPNN, and LSTM. The experiment results demonstrate that the proposed model achieves higher prediction accuracy compared to the other seven competing models. Finally, the future nuclear and wind power generation from 2023 to 2030 are predicted by adopting the IANGBM(1,1) model. For the next 8 years, nuclear power generation will maintain stable development, while wind energy power generation will grow rapidly.
Asunto(s)
Algoritmos , Dinámicas no Lineales , Fuentes Generadoras de Energía , Viento , ChinaRESUMEN
This paper aims to create a high-quality surface based on the linear contact material removal mechanism. For this paper, a piezo-driven, flexure-based micro-motion stage was developed for the vibration-assisted roll-type precision polishing system. Meanwhile, the compliance matrix method was employed to establish the amplification ratio and compliance model of the flexure mechanism. The dimensions of the mechanism were optimized using the grey wolves optimization (GWO) algorithm, aiming to maximize the natural frequencies. Using the optimal parameters, the established models for the mechanical performance evaluation of the flexure stage were verified with the finite-element method. Through closed-loop test, it was proven that the proposed micro-motion stage performs well in positioning micro motions. Finally, high quality surface using silicon carbide (SiC) ceramic with 36 nm Sa was generated by the independently developed vibration-assisted roll-type polishing machine to validate the performance of the established polishing system.