Multiple kernel support vector machine short-term load forecasting based on multi-source heterogeneous integration of load factors

In this paper we present a novel method developed from multiple kernel function for short term load forecasting to integrate multi-source heterogeneous load factors in big data. Nine kinds of load factors were selected as multi-source heterogeneous factors. In the proposed method, three algorithms (the sample distribution method, single variable method and rank space diversity method) were adopted to establish the optimal multiple kernel functions to integrate the load factors. Experimental results show that the average relative error of multiple kernel SVM is smaller than single kernel SVM, and the accuracy of multiple kernel SVM model based on double layer multiple kernel learning algorithm and lp norm is the highest. Therefore, multiple kernel SVM can deal with the multi-source heterogeneous data in the load forecasting effectively, and the speed and accuracy of load forecasting can be improved by parallel processing.

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