From C.elegans to Liquid Neural Networks: A Robust Wind Power Multi-Time Scale Prediction Framework

AI, especially deep learning algorithm, has proved its potential in wind power prediction; however, the lack of explainability is the main concern to address and this work is the first to investigate the emerging Liquid Neural Network (LNN) to provide necessary transparency in wind power prediction. LNN utilizes the mathematical abstraction of C.elegans and demonstrates liquid/robust behavior in learning and estimation for unseen data. For comparative analysis, the LNN family (i.e., closed form continuous (CfC), Liquid Time Constant) and state-of-the-art recurrent networks (e.g., LSTM and GRU) and 1D-CNN are considered, and the CfC neural network provides the best results on unseen data. CfC models with fully connected layers using only 25 neurons have provided superior results for wind power prediction in different time spans, resolutions, and number of variables.