An Improved Genetic-Fuzzy Algorithm and Its Application in Short- Term Load Forecasting

摘要

The identification of a fuzzy system model consists of two major phases: structure identification and parameter identification. The aim of this paper is to determine the main aspects involved in developing a flexible method able to learn and optimize both structure and the parameters of fuzzy inference system. We take a Sugeno fuzzy system with network structure as the initial forecasting model, and the improved genetic algorithm is used to confirm its structure and parameters. A case study of load forecasting in a certain power network shows that the model proposed here has a better fitting precision.