Least Squares-support Vector Machine Load Forecasting Approach Optimized by Bacterial Colony Chemotaxis Method
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The development of smart grid and electricity market requires more accurate and faster short-term load forecasting.A least squares-support vector machine(LS-SVM) based on bacterial colony chemotaxis optimization(BCC) algorithm was proposed that improving the computing accuracy and speed through a novel category of bionic algorithm,and determining the hyper-parameters of LS-SVM through BCC optional algorithm fleetly and reasonably.It shows that the BCC-LS-SVM algorithm not only has strong global search capability,but also is easy to implement.A load forecast empirical example has shown that compared with back-propagation artificial neural networks and single LS-SVM algorithm,BCC-LS-SVM algorithm can achieve higher prediction accuracy,better computational speed,and which is more suitable for short term load forecasting in China.