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• W2012302230 abstract "In competitive environment of deregulated electricity market, short-term load forecasting (STLF) is a major discussion for efficient operation of power systems. Therefore, the area of electricity load forecasting is still essential need for more accurate and stable load forecast algorithm. However, the electricity load is a non-linear signal with high degree of volatility. In this paper, a new forecasted method based on neural network (NN) and chaotic intelligent feature selection is presented. The proposed feature selection method selects the best set of candidate input which is used as input data for the forecasted. The theory of phase space reconstruction under Taken’s embedding theorem is used to prepare candidate features. Then, candidate inputs relevance to target value are measured by using correlation analysis. Forecast engine is a multilayer perception layer (MLP) NN with hybrid Levenberg–Marquardt (LM) and Differential Evolutionary (DE) learning algorithm. The proposed STLF is tested on PJM and New England electricity markets and compared with some of recent STLF techniques." @default.
• W2012302230 created "2016-06-24" @default.
• W2012302230 creator A5025414243 @default.
• W2012302230 creator A5070213603 @default.
• W2012302230 creator A5085087116 @default.
• W2012302230 date "2014-11-01" @default.
• W2012302230 modified "2023-09-27" @default.
• W2012302230 title "A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection" @default.
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• W2012302230 doi "https://doi.org/10.1016/j.ijepes.2014.05.036" @default.
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