The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the\nforecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is\ndeveloped to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In\nthe first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established\ncoverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for\nrenewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster\nare expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is\ncarried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy\nmanagement problem with more accuracy and robustness.
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