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Microgrid Data Learning Algorithm Optimization by Leroy EMANEZE is a document available to read on EtoBox.
The paper discusses the application of an improved data classification algorithm for microgrid load data, addressing the challenges posed by the increasing complexity and volume of such data. It proposes a parallelized back propagation neural network (BPNN) algorithm enhanced by combining extreme learning, simulated annealing, and artificial fish swarm algorithms to optimize load classification. This approach aims to improve real-time data processing for effective load management and microgrid optimization.
- Author
- Leroy EMANEZE
- Language
- EN