Abstract
Price forecasting in competitive electricity markets plays a crucial role for any decision making. This is a difficult task since price time series are non-stationary, and with variable mean and variance, and also have periodic monthly and seasonal behavior. This paper introduces an approach to forecast several-hours-ahead electricity locational marginal price (LMP) using locally linear neuro-fuzzy (LLNF) model for the PJM market. The autocorrelation method is used to make the appropriate input vectors. The LLNF model leads to more accurate results compared to the Multi Layer Perceptron (MLP) neural network.
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