Forecasting for smart grid applications with Higher Order Neural Networks

This work presents the design of a neural network which combines higher order terms in its input layer and an Extended Kalman Filter (EKF) based algorithm for its training. The neural network based scheme is defined as a Higher Order Neural Network (HONN) and its applicability is illustrated by means of time series forecasting for three important variables present in smart grids: Electric Load Demand (ELD), Wind Speed (WS) and Wind Energy Generation (WEG). The proposed model is trained and tested using real data values taken from a microgrid system in the UADY School of Engineering. The length of the regression vector is determined via the Lipschitz quotients methodology.