Abstract
A RBF neural network considering the critic mechanism is introduced to predict the system marginal price (SMP). The system consists of three elements, which are a predictor, an evaluator and a learning machine. The predictor is used to forecast the future SMP. The estimator is used to evaluate the prediction's validity. The explorer is used to determine the predictive step length. And the learning machine is used to keep the predictor self-learning. So the predictor can conform to SMP by self-learning and be in a good forecasting state. The simulation shows the proposed method has higher forecasting accuracy in irregular SMP cases than the conventional method has.
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