Abstract
Integrated with rough set theory and fuzzy neural network, this article presents a hybrid model for short-term load forecasting. The genetic algorithm is used to find the minimum reduct which is relevant to electric loads, and the crude domain knowledge extracted from the elementary data set is applied to design the structure and weights of the network. It is testified by the simulation results that the rough fuzzy neural network has better precision and convergence than the traditional fuzzy neural network. Moreover, it becomes easier to understand the transferring way of knowledge in neural network.
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