A New Hybrid Algorithm for Short Term Load Forecasting
نویسندگان
چکیده
In restructuring the electric power industry, the load had an important role for market managers and participants when they develop strategies or make decisions to maximize their profit. Therefore, accurate short term load forecasting (STLF) becomes more and more vital for all market participants such as customer or producer in competitive electricity markets. In this paper, a new hybrid algorithm is proposed to forecast day-ahead load signals. This load-forecasting algorithm works based on two stage feature selection (TSFS) method, discrete wavelet transform (DWT), least squares support vector machine (LSSVM) optimized by a modified artificial bee colony (ABC) using chaotic local search (CLS) method namely chaotic artificial bee colony (CABC). The numerical simulation results show that the proposed hybrid algorithm improves the accuracy of electricity load forecasting in different ceases in comparison to previously-known classical and intelligent methods.
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