A new short-term load forecasting approach using self-organizing fuzzy ARMAX models

This paper proposes a new self-organizing model of fuzzy autoregressive moving average with exogenous input variables (FARMAX) for one day ahead hourly load forecasting of power systems. To achieve the purpose of self-organizing the FARMAX model, identification of the fuzzy model is formulated as a combinatorial optimization problem. Then a combined use of heuristics and evolutionary programming (EP) scheme is relied on to solve the problem of determining optimal number of input variables, best partition of fuzzy spaces and associated fuzzy membership functions. The proposed approach is verified by using diverse types of practical load and weather data for Taiwan Power (Taipower) systems. Comparisons are made of forecasting errors with the existing ARMAX model implemented by the commercial SAS package and an artificial neural networks (ANNs) method.

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