Mutual Information Based Input Selection in Neuro-Fuzzy Modeling for Short Term Load Forecasting of Iran National Power System

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Neuro-fuzzy modeling has played successful role in various applications over nonlinear time series prediction. In modeling, irrelevant inputs cause the deterioration of performance. Therefore, to have an accurate model, some strategies are needed to choose a set of most relevant inputs. Mutual information (MI) is very effective in evaluating the relevance of each input from the aspect of information theory. This paper presents neuro-fuzzy model with locally linear model tree (LoLiMoT) learning algorithm for the STLF of Iran national power system (INPS). Proper inputs which consider historical data of INPS are selected by MI.