摘要
More and more microgrid projects are put into operation and completed, and the load data are becoming more and more multidimensional and massive. This requires effective classification of load data. Most of the traditional processing methods are based on neural network to classify the grid data. However, with the development of microgrid, the traditional neural network algorithm is having a hard time meeting the requirement of the classification and operation of massive microgrid data. In this paper, the back propagation neural network (BPNN) algorithm is parallelized based on the traditional reverse neural network algorithm. Multiple algorithms are applied for data learning, for example, the combined application of extreme learning algorithm and simulated annealing algorithm, artificial fish swarm algorithm and other evolutionary algorithms. The input variables in BPNN are optimized in the network training process. After adding the algorithm fitness evaluation function, the combined algorithm of improved back propagation neural network algorithm came out. It is most in line with the real-time data of power grid by means of root mean square error. This result could provide data support and theoretical basis for load management, microgrid optimization, energy storage management and electricity price modeling of microgrid.
More and more microgrid projects are put into operation and completed, and the load data are becoming more and more multidimensional and massive. This requires effective classification of load data. Most of the traditional processing methods are based on neural network to classify the grid data. However, with the development of microgrid, the traditional neural network algorithm is having a hard time meeting the requirement of the classification and operation of massive microgrid data. In this paper, the back propagation neural network (BPNN) algorithm is parallelized based on the traditional reverse neural network algorithm. Multiple algorithms are applied for data learning, for example, the combined application of extreme learning algorithm and simulated annealing algorithm, artificial fish swarm algorithm and other evolutionary algorithms. The input variables in BPNN are optimized in the network training process. After adding the algorithm fitness evaluation function, the combined algorithm of improved back propagation neural network algorithm came out. It is most in line with the real-time data of power grid by means of root mean square error. This result could provide data support and theoretical basis for load management, microgrid optimization, energy storage management and electricity price modeling of microgrid.
作者
Wenxing Jin
Haiming Li
Guixue Cheng
Wenxing Jin;Haiming Li;Guixue Cheng(Institute of Computer Science and Technology, Shanghai Electric Power University, Shanghai)
