A method to select optimal multiple kernels developed from multiple kernel function is proposed for short-term load forecasting in the big data environment of smart grid

multi-source heterogeneous load factors taken into account. The multiple kernel function is able to describe the distribution characteristics of the factors

cope with their variations and improve the accuracy of load forecasting. Load factors such as historical load

air temperature

air pressure

relative humidity

rainfall

wind direction

wind speed

holidays and electricity price are selected as multi-source heterogeneous factors. Three methods(the sample distribution method

single variable method and rank space diversity method)are adopted to establish optimal multiple kernels

and parallel multiple kernel support vector machine(SVM)load forecasting algorithm is based on double layer multi kernel learning algorithm. A Hadoop cluster is built for conducting experiments of short-term load forecasting. Experimental results show that the average relative error of multiple kernel SVM is smaller than single kernel SVM's

and the accuracy of multiple kernel SVM model based on double layer multiple kernel learning algorithm and norm is the highest. Therefore

multiple kernel SVM can tackle the multi-source heterogeneous data in the load forecasting effectively

and the speed and accuracy of load forecasting can be improved by parallel processing. This work is supported by National Natural Science Foundation of China(No. 51407116)and National Key Technologies R&D Program(No. 2015BAA01B02).