Wind Power Forecasting using Type-2 Fuzzy Control and its Optimization based on Artificial Neural Network for Small Scale Wind Power - Chatterjee, Arunava (Peneliti Teknologi Teknik Indonesia, 2024)
Improving the efficiency and economic feasibility of variable renewable resources, wind speed forecasting can improve the quality of wind energy generation. By using the properties of wind-related factors, this work provides a new model for wind energy forecasting for electrical power generation at an onshore location in India. The model, which employs an Interval Type-2 fuzzy logic system (IT2FS), takes inputs of wind features and forecasts wind power. Further, an artificial neural network (ANN) is chosen as the adjustment model for optimization in the architecture. The neural network begins evaluating its performance using a different number of hidden-layer neurons. The ANN-based hybrid model outperforms other models according to comparisons drawn from statistical indices. The usage of this adjustment model of forecasting is shown to be quite helpful in predicting the wind power for driving fractional kW loads using wind-based generation techniques.