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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
As the global demand for renewable energy continues to grow, wind energy, as a clean and renewable form of energy, has gradually received widespread attention. However, due to the instability and uncontrollability of wind energy, wind power power prediction has become an important research field in the wind power industry. Accurately predicting wind power power can help grid managers better dispatch power resources and improve the reliability and stability of the power system.
In the past few decades, artificial intelligence technology has been widely used in wind power prediction. Among them, BP neural network is a commonly used prediction model with strong nonlinear modeling capabilities. However, BP neural network easily falls into the local optimal solution during the training process, resulting in low prediction accuracy. To solve this problem, researchers have proposed many optimization algorithms to improve the performance of BP neural networks.
The slime mold algorithm (SMA) is an emerging optimization algorithm inspired by the biological world. It simulates the growth and reproduction process of slime molds in nature, and finds the optimal solution through information exchange between slime molds. The SMA algorithm has global search capabilities and strong robustness, and can effectively prevent the BP neural network from falling into the local optimal solution.
In the BP neural network optimized based on the slime mold algorithm, it is first necessary to determine the structure and parameters of the neural network. Usually, the structure of neural network includes input layer, hidden layer and output layer. The input layer receives meteorological data such as wind speed and wind direction, the hidden layer converts the input signal into a nonlinear output through the activation function, and the output layer outputs the prediction result of wind power power. After determining the structure of the neural network, the SMA algorithm needs to be used to optimize the weights and thresholds of the neural network.
The optimization process of the SMA algorithm includes steps such as initialization, slime migration, slime reproduction, and slime update. During the initialization phase, parameters such as the position and viscosity of the slime mold are randomly generated. Then, based on the interaction between slime molds, slime mold migration and reproduction operations are performed to find a better solution. Finally, in the slime mold update phase, the location and viscosity of the slime mold are updated based on the fitness value of the slime mold. Through multiple iterations, the SMA algorithm can gradually optimize the weights and thresholds of the BP neural network and improve the accuracy of wind power power prediction.
Experimental results show that the BP neural network optimized based on the slime mold algorithm has high accuracy and stability in wind power power prediction. Compared with the traditional BP neural network, the optimized neural network can better capture the characteristics and changing trends of wind energy and improve the prediction accuracy. In addition, the SMA algorithm also has fast convergence speed and strong robustness, and can adapt to different wind power power prediction problems.
In summary, the SMA optimized BP neural network based on the slime mold algorithm is an effective wind power power prediction method. It can overcome the local optimal solution problem of BP neural network and improve the prediction accuracy and stability. In the future, we can further explore the application of the slime mold algorithm in other fields and combine it with other optimization algorithms to further improve the performance of wind power power prediction.
Part of the code
%_______________________________________________________________% % Gray Wolf Optimizer (GWO) source codes version 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: [email protected] % % [email protected] % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis % % Gray Wolf Optimizer, Advances in Engineering % % Software, in press, % % DOI: 10.1016/j.advengsoft.2013.12.007 % % % %____________________________________________________________% % This function initializes the first population of search agents function Positions=initialization(SearchAgents_no,dim,ub,lb) Boundary_no= size(ub,2); % number of boundaries % If the boundaries of all variables are equal and user enter a signle % number for both ub and lb if Boundary_no==1 Positions=rand(SearchAgents_no,dim).*(ub-lb) + lb; end % If each variable has a different lb and ub if Boundary_no>1 for i=1:dim ub_i=ub(i); lb_i=lb(i); Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i) + lb_i; end end
 Fan Yuanyuan, Meng Difei, Sang Yingjun, et al. Elevator fault prediction method based on improved slime mold algorithm optimizing Elman neural network. CN202211341256.X[2023-09-22].