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Performance of the novel rough fuzzy-neural network on short-term load forecasting

A hybrid model integrating with rough set theory and fuzzy neural network is presented for short-term load forecasting. A multiobjective genetic algorithm is used to learn automatically the knowledge of historical data set and find the best factors that are 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 neural network. Simulation results demonstrate 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.