In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p-power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.

As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced. Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model. Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34%. Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.INDEX TERMS Differential evolutionary algorithm, Kernel extreme learning machine, k fold cross validation, wind power prediction.

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Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.