A Belief Rule Base with Fuzzy Reference Value for Wind Power Generation Forecasting

Wind power generation forecasting is a key technology for wind power projects. It directly determines the stability of grid integration and the accuracy of power dispatching. The interval belief rule base (IBRB) is an uncertainty modeling method; it can be applied to wind power generation forecasting. On the one hand, IBRB uses fixed interval matching. This method tends to cause boundary jumps when predicting continuously variable parameters, which threatens the stability of the grid integration. On the other hand, IBRB underutilizes the correlation information of adjacent intervals in modeling, and its rule activation mechanism limits expressions of complex generation mechanisms. To address these issues, a method based on belief rule base with fuzzy reference value (BRB-f) for wind power generation forecasting is proposed. Firstly, the method replaces fixed interval matching with fuzzy membership functions to reduce the impact of wind power output fluctuations on the grid. Then, through a multi-rule-weighted fusion mechanism and optimization algorithms, it improves the accuracy of scheduling under complex generation mechanisms. Finally, the effectiveness and accuracy of the model are validated using a wind turbine power generation forecasting dataset. It provides a better method choice to ensure grid integration safety and enhance the scientific basis of power dispatch decisions.