Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

Abstract

Use of wind power as one of renewable resources of energy has been growing quickly all over the world. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction aims to support the operation of large power systems or microgrids in the scope of the intraday resources scheduling model, namely with a time horizon of 5-10 minutes, and this can efficiently uphold transmission and distribution system operators to improve the power network control and management. A Shortterm ANN based wind power prediction model, for day-ahead energy management and scheduling in microgrids, is developed utilizing a real database of 12 months with 10 minutes time interval data from measured information of online supervisory control and data acquisition (SCADA) of Goldwind microgrid wind turbine system (Beijing, China) as well as Numerical Weather Prediction (NWP). The ANN predicted wind power has been compared with the actual power of Goldwind wind turbine system. The predicted wind power has shown acceptable agreement with the actual power output that has been recorded by the SCADA which confirms the robustness and accuracy of the ANN wind power forecasting model developed in this research paper.