Application of rough set theory and artificial neural network for load forecasting

A hybrid model Integrating with rough set theory and fuzzy neural network is presented for short-term load forecasting. Multi-objective 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. Index Terms--Rough set, Fuzzy-neural network, Multiobjective genetic algorithm, Short-term load forecasting, Data miningThis work is supported by NSF of Zhejiang Province, China (No. 502053) Li Feng is a Ph.D. student in College of electrical engineering,

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Application of rough set theory and artificial neural network for load forecasting

Cited by 5 publications

References 10 publications

Short-term load forecasting based on a rough fuzzy-neural network

Short-term load forecasting based on a rough fuzzy-neural network

Genetic algorithms and rough fuzzy neural network-based hybrid approach for short-term load forecasting

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

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