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In this paper, the establishment of the neural network model of forecasting short-term power load in an electric power grid is studied. Basing on the model, the BP algorithm for power load is explored. The research on BP network model includes determining the hidden layer number, hidden layer nodes number, training frequency and accuracy of learning rate. In this paper, we focus on that how to give...
Considering the chaotic characteristic of power system load, a method based on bee evolution modifying particle swarm optimization (BEMPSO) and chaotic neural network is presented for power system load forecasting to improve precision. In this paper, builds the chaotic neural network model and integrates bee evolution modifying with particle swarm optimization. The novel BEMPSO algorithm is proposed...
As a representative method of swarm intelligence particle swarm optimization (PSO) is an algorithm for search the multidimensional complex space through cooperation and competition among the individuals in a population of particles. A novel modified particle swarm optimization (MPSO) algorithm is proposed. The MPSO is determined by linearly decreasing inertia weight and constriction factor weight...
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