The development and deployment of an effective wind speed forecasting technology can improve the stability and safety of power systems with significant wind penetration. However, due to the wind's unpredictable and unstable qualities, accurate forecasting of wind speed and power is extremely challenging. Several algorithms were proposed for this purpose to improve the level of forecasting reliability. A common method for making predictions based on time series data is the long short-term memory (LSTM) network. This paper proposed a machine learning algorithm, called adaptive dynamic particle swarm algorithm (AD-PSO) combined with guided whale optimization algorithm (Guided WOA), for wind speed ensemble forecasting. The proposed AD-PSO-Guided WOA algorithm selects the optimal hyperparameters value of the LSTM deep learning model for forecasting purposes of wind speed. In experiments, a wind power forecasting dataset is employed to predict hourly power generation up to forty-eight hours ahead at seven wind farms. This case study is taken from the Kaggle Global Energy Forecasting Competition 2012 in wind forecasting. The results demonstrated that the AD-PSO-Guided WOA algorithm provides high accuracy and outperforms a number of comparative optimization and deep learning algorithms. Different tests' statistical analysis, including Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), confirms the accuracy of proposed algorithm.

This paper deals with the form and the periodicity of the solutions of the max-type system of difference equationswhereA n / n2N0 and .B n / n2N0 are positive two-periodic sequences and initial values x 0 , x 1 , y 0 , y 1 2 .0, C1/.

In this paper, the solutions of the recursive sequences x n+1