Development of competitive power markets has emerged an increasing tendency to forecast the future prices among both the producers and the consumers with the major aim of profit maximization. A Least-Square Support Vector Machines approach, in combination with a hybrid Genetic Algorithm based optimization is proposed in this paper to forecast Market Clearing Prices in two different power markets. The forecasting results are compared to a select variety of previously proposed methods such as MLP, ARIMA, WAVELET- ARIMA, Neuro-Fuzzy and Time Series based models. The performed comprehensive comparison demonstrates the remarkable accuracy and effectiveness of the proposed method.
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