New fluorescent dyes based on 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) and functionalized with a free carboxyl group have been conveniently synthesized from pyrroles and dicarboxylic anhydrides in one-pot reactions. Their spectral properties in different solvents showed little effect of solvatochromism (<10 nm). The methyl groups on the BODIPY skeleton benefit the fluorescence quantum yields (Phi(f) up to 0.80 in water) but affect the photostability of the dyes. Photooxidation and photodegradation experiments suggest that dyes 1a and 2a exhibit excellent photostability, especially in water, and several factors were taken into account to elucidate the experimental phenomena. Dyes 1c and 2c, derived from 1a and 2a via the esterification of NHS (N-hydroxysuccinimidyl ester), can be easily acquired in high yields (>90%). Single crystal X-ray structures of dyes 2c and 3a are also obtained and discussed. The fluorescence labeling of BSA and followed prestaining method for gel electrophoresis of BSA demonstrate that the protein can be directly observed by naked eyes at as low as 2 ng level under a normal UV fluorescence electrophorogram gel image system.

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Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind power, wind speed and temperature, the autoregressive moving average (ARMA) prediction model and the support vector machine (SVM) prediction model are established, particle swarm optimization (PSO) algorithm is involved for parameter optimization of SVM model. Furthermore, a hybrid PSO-SVM-ARMA prediction model based on ARMA and PSO-SVM model is illustrated for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights. Moreover, with the basis of clustering theory, time series are clustered to examine the effective dataset for wind power prediction, and a clustered hybrid PSO-SVM-ARMA (C-PSO-SVM-ARMA) wind power prediction model is prospectively proposed. In case study, different prediction models are carried out and the prediction performance is examined based on different evaluation indices, the C-PSO-SVM-ARMA model shows better performance for wind power prediction with computational efficiency and satisfying precision.INDEX TERMS Autoregressive moving average (ARMA) model, clustered hybrid wind power prediction model, clustering method, particle swarm optimization (PSO), support vector machine (SVM).

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