Wind Power Prediction Using a Dynamic Neuro-Fuzzy Model

Abstract

A Dynamic Neuro-fuzzy Model (Dynamic Neuro-fuzzy Wind Predictor, DNFWP) is proposed for wind power prediction. The fuzzy rules in DNFWP consist of a typical antecedent part with static inputs, while the consequent part is a small three-layer neural network, incorporating unit feedback connections at the outputs of the neurons of the hidden layer. The inclusion of internal feedback targets to capture the intrinsic temporal relations of the dataset, while maintaining the local modeling approach of traditional fuzzy models. Each rule in DNFWP represents a local model, and the fuzzy rules operate cooperatively through the defuzzification process. The fuzzy rule base is extracted employing the Fuzzy C-means clustering algorithm, and the consequent neural networks’ weights are tuned by the use of Dynamic Resilient Propagation. Two cases with datasets of different volumes are tested and the performance of DNFWP is very promising, according to the results attained using a series of metrics like Root Mean Squared Error, Mean Absolute Error, and the r-squared statistic. The dynamic nature of the predictor allows it to operate effectively with a single input, thus rendering a feature selection phase unnecessary. DNFWP is compared to Machine Learning-based and Deep Learning-based counterparts, such that its prediction capabilities along with its reduced parametric complexity are highlighted.