A class of neural adaptive FIR filters for complex-valued load prediction

Load prediction is a necessity in a deregulated electrical energy sector. It is important financially and technically. In order to cope with nonlinear and non stationary character of a load signal, an efficient adaptive predictor should be employed. Also, power utilities manage load information as a complex-valued signal. To this cause, performance of a class of complex-valued gradient descent (GD) neural adaptive finite impulse response (FIR) filters is analyzed. It is shown that fully complex nonlinear GD algorithms have the best performance in a load prediction task. To support the analysis, experiments are carried out on the test load signal, metered on a medium voltage feeder.