Abstract
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.
Highlights
IntroductionMicrogrids combined with new energy technologies have been increasingly applied to our daily lives, and power load information has begun to show an increase in the proportion of new energy sources, diversified
The back propagation neural network (BPNN) algorithm is parallelized based on the traditional reverse neural network algorithm
Microgrids combined with new energy technologies have been increasingly applied to our daily lives, and power load information has begun to show an increase in the proportion of new energy sources, diversified
Summary
Microgrids combined with new energy technologies have been increasingly applied to our daily lives, and power load information has begun to show an increase in the proportion of new energy sources, diversified. Decision-making and calculation in the field of power load have experienced advances from original expert analysis and decision-making, to neural network applications, and to improved artificial intelligence algorithms that combine neural networks with multiple clustering methods. It should be noted that the above-mentioned power load classification methods all have the most applicable power energy fields. It aims to use artificial intelligence to classify loads in parallel for different real-time power loads of microgrids, and to divide the shallow microgrid data structure. The feature combination forms a high-level data structure, and the clustered data is used for parallel computing and classification of extreme learning algorithms. The most suitable real-time microgrid data classification decision-making method is found through parallel multiple data in the form of fitness preference
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