Revisiting the modeling of wind turbine power curves using neural networks and fuzzy models: an application-oriented evaluation

Wind turbine power curve (WTPC) modeling from measured data is essential to predict the power generation from wind farms. Polynomial regression is commonly the first choice for this purpose, but there are other more powerful alternatives based on neural networks and fuzzy algorithms, for instance. Despite the existence of previous applications of such learning algorithms to WTPC modeling, a critical analysis of their performances has not yet been carried out while taking into into account both quantitative and quantitative aspects. Quantitative figures of merit include the root-mean-square error (RMSE) and R-squared ( $$R^2$$ ), whereas qualitative approaches are often based on simple visual inspection. In this context, this work reports the results of a comprehensive performance comparison involving the estimation of WTPC. The study comprises three neural-network-based models, that is, multilayer perceptron (MLP), radial basis function (RBF), and extreme learning machine (ELM); as well two fuzzy-logic-based models, that is, Takagi-Sugeno-Kang (TSK) and adaptive network fuzzy inference system (ANFIS). Using two real-world challenging data sets, it is possible to evaluate how the models perform concerning the accuracy of the curve fitting, sensitivity to parameter initialization, and occurrence of pathological solutions. Relevant issues, such as hyperparameter settings and data normalization are also addressed. The obtained results confirm the fact that the model selection should not rely only on quantitative performance indices. Thus, it is reasonable to state that the design of general-purpose modeling tools such as the ones evaluated in this work should incorporate domain-specific knowledge to provide good accuracy associated with reliable results.