Title :
Locational marginal price forecasting by locally linear neuro-fuzzy model
Author :
Sarafraz, Fahimeh ; Ghasemi, Hassan ; Monsef, Hassan
Author_Institution :
Sch. of Electr. & Comput. Eng., Univ. of Tehran, Tehran, Iran
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.
Keywords :
decision making; forecasting theory; fuzzy set theory; neural nets; power markets; pricing; LLNF model; PJM market; autocorrelation method; decision making; electricity markets; locally linear neuro-fuzzy model; several hours-ahead electricity locational marginal price forecasting; Correlation; Electricity; Forecasting; Neurons; Predictive models; Testing; Training data; electricity market; locally linear neuro-fuzzy model; locational marginal price forecasting; multi layer perceptron neural network;
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2011 10th International Conference on
Conference_Location :
Rome
Print_ISBN :
978-1-4244-8779-0
DOI :
10.1109/EEEIC.2011.5874828
