Title :
A Hybrid Method of Clipping and Artificial Neural Network for Electricity Price Zone Forecasting
Author :
Mori, H. ; Awata, A.
Author_Institution :
Dept. of Electr. & Electron. Eng., Meiji Univ., Kawasaki
Abstract :
This paper proposes a new method for electricity price zone forecasting. The proposed method makes use of the clipping technique that is one of data mining techniques for simplifying the relationship between input and output variables. It expresses an output variable in binary number. Electricity price forecasting is difficult to handle due to the nonlinearity of time series. This paper predicts the one-step-ahead price zone. In this paper, the normalized radial basis function network is used as an artificial neural network (ANN) to evaluate the predicted price. The proposed method is tested for the electricity price in the New England power market
Keywords :
data mining; economic forecasting; power engineering computing; power markets; power system economics; pricing; radial basis function networks; ANN; New England power market; artificial neural network; clipping technique; data mining; electricity price zone forecasting; normalized radial basis function network; Artificial neural networks; Data mining; Economic forecasting; Hybrid power systems; Nonlinear systems; Power markets; Power system modeling; Predictive models; Radial basis function networks; Weather forecasting; Data Mining; Forecasting; Intelligent Systems; MLP; Neural Network Applications; Prediction Method; Time Series;
Conference_Titel :
Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on
Conference_Location :
Stockholm
Print_ISBN :
978-91-7178-585-5
DOI :
10.1109/PMAPS.2006.360234
