Artificial immune system for short-term electric load forecasting

Abstract

This paper proposes a novel model, based on the artificial immune system, to solve the problem of short-term load forecasting. An artificial immune system is trained to recognize antigens which encode sequences of load time series. The created immune memory is a representation of these sequences. In the forecast procedure a new incomplete antigen, containing only the first part of the sequence, is presented to the model. The second forecasted part of the sequence is reconstructed from activated antibodies. The model was verified using several real data examples of the short-term load forecast. © 2008 Springer-Verlag Berlin Heidelberg.