1. A method of energy management and short term load prediction in a power system using an off-line neural network and an on-line neural network and a load database having load data captured from the field, each neural network comprising a plurality of neurons to predict a short term load demand on the power system, comprising: (a) implementing a first prediction scheme that uses off-line neural network training to output first on-line load predictions, the first prediction scheme comprising: receiving by an off-line neural network historical load data from a load database; training the off-line neural network with the historical load data, resulting in a trained off-line neural network with tuned parameter values Θ containing weights and biases; loading short term load forecast data into the trained off-line neural network; generating first on-line load predictions by the trained off-line neural network; (b) implementing a second prediction scheme that uses on-line neural network training to output second on-line load predictions, the second prediction scheme comprising: initializing parameter values of an on-line neural network with the tuned parameter values Θ from the trained off line neural network; receiving by the on-line neural network current load data for a current time segment from the load database; adjusting the parameter values of the on-line neural network based on the current load data; training the on-line neural network with the current load data, resulting in a trained on-line neural network; loading short term load forecast data into the trained on-line neural network; generating second on-line load predictions by the trained on-line neural network; (c) calculating performance statistics of the first and second prediction schemes wherein the performance statistics are represented by counters, comprising: comparing the first on-line load predictions with actual load values and incrementing a first counter if the difference between the first on-line load predictions and the actual load values are within a predetermined range; comparing the second on-line load predictions with actual load values and incrementing a second counter if the difference between the second on-line load predictions and the actual load values are within a predetermined range; (d) selecting either the first or second on-line load predictions as final predicted load values based on a decision algorithm using the calculated performance statistics; and (e) scheduling energy generation of the power system based on the final predicted load values.